attention.py 320 KB
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# Copyright (c) 2022-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
# See LICENSE for license information.

"""Attention."""
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import collections
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from contextlib import nullcontext
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from importlib.metadata import version as get_pkg_version
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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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from dataclasses import dataclass, fields
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import numpy as np
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from packaging.version import Version as PkgVersion
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import torch
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import torch.nn.functional as F
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import transformer_engine_torch as tex
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import transformer_engine as te
from transformer_engine.pytorch.utils import get_cudnn_version
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from transformer_engine.pytorch.cpp_extensions import (
    cast_to_fp8,
    cast_from_fp8,
)
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from transformer_engine.pytorch.cpp_extensions.fused_attn import (
    fused_attn_fwd_qkvpacked,
    fused_attn_bwd_qkvpacked,
    fused_attn_fwd_kvpacked,
    fused_attn_bwd_kvpacked,
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    fused_attn_fwd,
    fused_attn_bwd,
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    QKVLayout,
    AttnBiasType,
    AttnMaskType,
    FusedAttnBackend,
)
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from transformer_engine.pytorch.fp8 import get_fp8_te_dtype
from transformer_engine.pytorch.float8_tensor import Float8Tensor
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from transformer_engine.pytorch.module import LayerNormLinear, Linear
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from transformer_engine.pytorch.module.base import TransformerEngineBaseModule
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from transformer_engine.pytorch.utils import (
    divide,
    attention_mask_func,
    split_tensor_along_dim,
    get_device_compute_capability,
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    get_default_init_method,
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)
from transformer_engine.pytorch.constants import (
    AttnMaskTypes,
    AttnTypes,
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    AttnBiasTypes,
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    QKVLayouts,
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    dist_group_type,
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    TE_DType,
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)
from transformer_engine.pytorch.softmax import FusedScaleMaskSoftmax
from transformer_engine.pytorch.distributed import (
    get_distributed_world_size,
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    get_distributed_rank,
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    checkpoint,
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    set_all_rng_states,
    CudaRNGStatesTracker,
    graph_safe_rng_available,
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    gather_along_first_dim,
    reduce_scatter_along_first_dim,
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)
from transformer_engine.pytorch.export import is_in_onnx_export_mode
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from transformer_engine.pytorch.jit import jit_fuser, no_torch_dynamo
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from transformer_engine.pytorch.graph import is_graph_capturing

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_flash_attn_version = PkgVersion(get_pkg_version("flash-attn"))
_flash_attn_version_required = PkgVersion("2.0.6")
_flash_attn_max_version = PkgVersion("2.5.8")
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_flash_attn_2_plus = _flash_attn_version >= PkgVersion("2")
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_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")
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_flash_attn_2_5_7_plus = _flash_attn_version >= PkgVersion("2.5.7")
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if _flash_attn_version >= _flash_attn_version_required:
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    from flash_attn.flash_attn_interface import flash_attn_varlen_func as flash_attn_forward_func
    from flash_attn.flash_attn_interface import _flash_attn_varlen_forward as _flash_attn_forward
    from flash_attn.flash_attn_interface import _flash_attn_varlen_backward as _flash_attn_backward
    from flash_attn_2_cuda import varlen_bwd as flash_attn_cuda_bwd
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META_QKV = tex.FP8FwdTensors.GEMM1_OUTPUT
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META_DQKV = tex.FP8BwdTensors.GRAD_OUTPUT1
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META_O = tex.FP8FwdTensors.GEMM2_INPUT
META_DO = tex.FP8BwdTensors.GRAD_INPUT2
META_S = tex.FP8FwdTensors.GEMM3_OUTPUT
META_DP = tex.FP8BwdTensors.GRAD_INPUT3
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# NVTE_DEBUG = 0/1 # disables/enables debug mode, default = 0
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_NVTE_DEBUG = int(os.getenv("NVTE_DEBUG", "0"))
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# 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"))
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_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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_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"))

_attention_backends = {
    "attention_params": None,
    "use_flash_attention": None,
    "use_fused_attention": None,
    "fused_attention_backend": None,
    "use_unfused_attention": None,
    "backend_selection_requires_update": False,
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}
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@dataclass(eq=True)
class AttentionParams:
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    """
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    Attention parameters used to determine which backend to be used.
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    Parameters
    ----------
    qkv_type: Union[torch.Tensor, Float8Tensor], default = `torch.Tensor`
        Type of query/key/value tensors, {`torch.Tensor`, `Float8Tensor`}.
    qkv_dtype: torch.dtype, default = `torch.bfloat16`
        Data type of query/key/value tensors.
    qkv_layout: str, default = "sbh3d"
        Query/key/value tensor memory layout.
    batch_size: int, default = 1
        Batch size.
    num_heads: int, default = 16
        Number of attention heads in the query tensor.
    num_gqa_groups: int, default = 16
        Number of attention heads in key and value tensors.
    max_seqlen_q: int, default = 128
        Maximum sequence length of the query tensor.
    max_seqlen_kv: int, default = 128
        Maximum sequence length of the key and value tensors.
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    head_dim_qk: int, default = 64
        The size of each attention head in query and key tensors.
    head_dim_v: int, default = 64
        The size of each attention head in the value tensor.
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    attn_mask_type: str, default = `no_mask`
        Attention mask type, {`no_mask`, `padding`, `causal`, `padding_causal`,
        `causal_bottom_right`, `padding_causal_bottom_right`, `arbitrary`}
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    window_size: Tuple[int, int], default = None
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        Sliding window attention size.
    alibi_slopes_shape: Optional[Union[torch.Size, List]], default = `None`
        Tensor shape of :attr:`alibi_slopes` in `DotProductAttention`.
    core_attention_bias_type: str, default = `no_bias`
        Attention bias type, {`no_bias`, `pre_scale_bias`, `post_scale_bias`, `alibi`}.
    core_attention_bias_shape: str, default = `1hss`
        Attention bias shape, {`1hss`, `b1ss`, `bhss`}.
    core_attention_bias_requires_grad: bool, default = `True`
        Whether attention bias requires gradient.
    pad_between_seqs: bool, default = `False`
        Whether there is padding between sequences in a batch.
        This only applies to `qkv_format=thd`.
    attention_dropout: float, default = 0.0
        Attention dropout.
    context_parallel: bool, default = `False`
        Whether context parallelism is used or not.
    deterministic: bool, default = `False`
        Whether to run `DotProductAttention` with determinism or not.
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    is_training: bool, default = `True`
        Whether in training mode (`True`) or inference mode (`False`)
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    fp8: bool, default = `False`
        Whether `DotProductAttention` is in an `fp8_autocast` region.
    fp8_meta: Optional[Dict[str Any]], default = `None`
        The FP8 metadata tensor of `DotProductAttention`.
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    """

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


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


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


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"
        + str(
            (lambda x, y: x * 10 + y)(device_compute_capability[0], device_compute_capability[1])
        ),
        "flash_attn_version": _flash_attn_version,
        "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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    # Filter: Environment variables
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    global _NVTE_FLASH_ATTN, _NVTE_FUSED_ATTN, _NVTE_UNFUSED_ATTN
    _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"))
    use_flash_attention = _NVTE_FLASH_ATTN
    use_fused_attention = _NVTE_FUSED_ATTN
    use_unfused_attention = _NVTE_UNFUSED_ATTN
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    if not use_flash_attention:
        logger.debug("Disabling FlashAttention due to NVTE_FLASH_ATTN=0")
    if not use_fused_attention:
        logger.debug("Disabling FusedAttention due to NVTE_FUSED_ATTN=0")
    if not use_unfused_attention:
        logger.debug("Disabling UnfusedDotProductAttention due to NVTE_UNFUSED_ATTN=0")

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

    # Filter: Compute capability
    if device_compute_capability < (8, 0):
        if use_flash_attention:
            logger.debug("Disabling FlashAttention as it requires compute capability sm80+")
            use_flash_attention = False
        if use_fused_attention:
            logger.debug("Disabling FusedAttention as it requires compute capability sm80+")
            use_fused_attention = False

    # Filter: Data type
    if use_flash_attention and (
        qkv_dtype not in [torch.bfloat16, torch.float16] or qkv_type == Float8Tensor
    ):
        logger.debug(
            "Disabling FlashAttention due to unsupported QKV data type. "
            "Supported: qkv_type = torch.Tensor, qkv_dtype = {torch.bfloat16, torch.float16}. "
            "Found: qkv_type = %s, qkv_dtype = %s.",
            qkv_type,
            qkv_dtype,
        )
        use_flash_attention = False
    if use_fused_attention and (qkv_dtype not in [torch.bfloat16, torch.float16]):
        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

    # Filter: Execution type
    if fp8 and fp8_meta["recipe"].fp8_dpa:
        if use_flash_attention:
            logger.debug("Disabling FlashAttention as it does not support FP8")
            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:
        logger.debug("Disabling FlashAttention as it does not support MLA.")
        use_flash_attention = False
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    if use_flash_attention and (
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        head_dim_qk > 256
        or head_dim_qk % 8 != 0
        or (head_dim_qk > 192 and device_compute_capability not in ((8, 0), (9, 0)))
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    ):
        logger.debug(
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            "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,
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            ".".join([str(i) for i in device_compute_capability]),
        )
        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:
            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]"
            )
            use_flash_attention = 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:
        if "bottom_right" in attn_mask_type:
            logger.debug(
                "Disabling FlashAttention as it does not support context parallelism with"
                " causal_bottom_right masking"
            )
            use_flash_attention = False
        elif "causal" in attn_mask_type and max_seqlen_q != max_seqlen_kv:
            logger.debug(
                "Disabling FlashAttention as it does not support context parallelism with causal"
                " masking for cross-attention"
            )
            use_flash_attention = False
        elif core_attention_bias_type not in ["no_bias", "post_scale_bias"]:
            logger.debug(
                "Disabling FlashAttention as it does not support context parallelism with bias type"
                " of %s",
                core_attention_bias_type,
            )
            use_flash_attention = False
        elif qkv_format == "thd" and core_attention_bias_type != "no_bias":
            logger.debug(
                "Disabling FlashAttention as it does not support context parallelism with attention"
                " bias for THD format"
            )
            use_flash_attention = False
    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
    # attn_mask_type               |     supported backends
    # -------------------------------------------------------------------
    # no_mask                      |     All
    # padding                      |     FlashAttention, FusedAttention
    # causal                       |
    #     self-attention           |     All
    #     cross-attention          |     FusedAttention
    # padding_causal               |
    #     self-attention           |     FlashAttention, FusedAttention
    #     cross-attention          |     FusedAttention
    # causal_bottom_right          |     All
    # padding_causal_bottom_right  |     FlashAttention, FusedAttention
    # arbitrary                    |     UnfusedDotProductAttention
    if attn_mask_type == "arbitrary":
        if use_flash_attention:
            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
    if use_unfused_attention and "padding" in attn_mask_type:
        logger.debug("Disabling UnfusedDotProductAttention for %s mask", attn_mask_type)
        use_unfused_attention = False
    if (
        use_flash_attention
        and _flash_attn_2_1_plus
        and attn_mask_type in ["causal", "padding_causal"]
        and max_seqlen_q != max_seqlen_kv
    ):
        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 (
        use_flash_attention
        and not _flash_attn_2_1_plus
        and attn_mask_type in ["causal_bottom_right", "padding_causal_bottom_right"]
        and max_seqlen_q != max_seqlen_kv
    ):
        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

    # Filter: Sliding window attention
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    #    backend                 |      window_size       | diagonal alignment
    # ---------------------------------------------------------------------------------
    # FlashAttention             | (-1, -1) or (>=0, >=0) | bottom right
    # FusedAttention             | (-1,  0) or (>=0, 0)   | top left
    # UnfusedDotProductAttention | (-1, -1) or (>=0, >=0) | both;
    #                            |                        | converts window_size to an 'arbitrary' mask
    if window_size is None:
        window_size = check_set_window_size(attn_mask_type, window_size)
    else:
        if use_fused_attention and (window_size[0] != -1 or window_size[1] not in [-1, 0]):
            if fp8 and (fp8_meta["recipe"].fp8_dpa or fp8_meta["recipe"].fp8_mha):
                logger.debug(
                    "Disabling FusedAttention as it does not support sliding window attention"
                    " for FP8"
                )
                use_fused_attention = False
            elif window_size[1] != 0 or attention_dropout != 0.0 or qkv_format == "thd":
                logger.debug(
                    "Disabling FusedAttention as it only supports sliding window attention "
                    "with causal mask, no dropout, and qkv_format = bshd/sbhd"
                )
                use_fused_attention = False
            elif context_parallel:
                logger.debug(
                    "Disabling FusedAttention as it does not support sliding window attention "
                    "with context parallelism"
                )
                use_fused_attention = False
            elif max_seqlen_q != max_seqlen_kv and attn_mask_type in [
                "no_mask",
                "padding",
                "causal_bottom_right",
                "padding_causal_bottom_right",
            ]:
                logger.debug(
                    "Disabling FusedAttention as it does not support sliding window attention "
                    "with attn_mask_type = %s for cross-attention",
                    attn_mask_type,
                )
                use_fused_attention = False
            elif "padding" in attn_mask_type:
                logger.debug(
                    "Disabling FusedAttention as it does not support sliding window attention "
                    "with attn_mask_type = %s",
                    attn_mask_type,
                )
                use_fused_attention = False
        if (
            use_flash_attention
            and (window_size[0] != -1 or window_size[1] not in [-1, 0])
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            and not _flash_attn_2_3_plus
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        ):
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            logger.debug(
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                "Disabling FlashAttention as sliding window attention requires flash-attn 2.3+"
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            )
            use_flash_attention = False

    # Filter: Attention bias
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    #    backend                 |      bias types              | ALiBi diagonal alignment
    # ---------------------------------------------------------------------------------
    # FlashAttention             | no_bias, alibi/alibi_slopes  | bottom right
    # FusedAttention             | no_bias, post_scale_bias     |
    #                            | alibi/alibi_slopes           | top left,
    #                            |                              | bottom_right (converts to a 'post_scale_bias' bias)
    # UnfusedDotProductAttention | no_bias, pre/post_scale_bias |
    #                            | alibi/alibi_slopes           | both; converts to a 'post_scale_bias' bias
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    if use_flash_attention and (
        core_attention_bias_type not in ["no_bias", "alibi"]
        or core_attention_bias_shape is not None
    ):
        logger.debug("Disabling FlashAttention for pre/post_scale_bias")
        use_flash_attention = False

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

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

    # Filter: cuDNN support
    fused_attention_backend = None
    if use_fused_attention:
        q_type = TE_DType[qkv_dtype]
        kv_type = q_type
        if fp8 and fp8_meta["recipe"].fp8_dpa:
            q_type = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
            kv_type = q_type
        fused_attention_backend = tex.get_fused_attn_backend(
            q_type,
            kv_type,
            QKVLayout[qkv_layout],
            AttnBiasType[fu_core_attention_bias_type],
            AttnMaskType[attn_mask_type],
            attention_dropout,
            num_heads,
            num_gqa_groups,
            max_seqlen_q,
            max_seqlen_kv,
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            head_dim_qk,
            head_dim_v,
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            window_size[0],
            window_size[1],
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        )
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        if fused_attention_backend == FusedAttnBackend["No_Backend"]:
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            logger.debug("Disabling FusedAttention as no backend supports the provided input")
            use_fused_attention = False
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            fused_attention_backend = None
        if (
            use_fused_attention
            and context_parallel
            and fused_attention_backend != FusedAttnBackend["F16_arbitrary_seqlen"]
        ):
            logger.debug(
                "Disabling FusedAttention as only sub-backend %s does not support "
                "context parallellism",
                int(fused_attention_backend),
            )
            use_fused_attention = False
            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
    if use_flash_attention and deterministic and not _flash_attn_2_4_1_plus:
        logger.warning(
            "Disabling FlashAttention as version <2.4.1 does not support deterministic "
            "execution. To use FlashAttention with deterministic behavior, "
            "please install flash-attn >= 2.4.1."
        )
        use_flash_attention = False
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    if use_fused_attention and deterministic:
        if fused_attention_backend == FusedAttnBackend["FP8"] and is_training:
            logger.debug("Disabling FusedAttention for determinism reasons")
            use_fused_attention = False
        if (
            fused_attention_backend == FusedAttnBackend["F16_arbitrary_seqlen"]
            and is_training
            and (
                device_compute_capability < (9, 0)
                or core_attention_bias_requires_grad
                or cudnn_version < (8, 9, 5)
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            )
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        ):
            logger.debug("Disabling FusedAttention for determinism reasons")
            use_fused_attention = False
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    # All available backends
    available_backends = [use_flash_attention, use_fused_attention, use_unfused_attention]
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    logger.debug(
        "Available backends = {FlashAttention=%s, FusedAttention=%s%s,"
        " UnfusedDotProductAttention=%s}",
        bool(available_backends[0]),
        bool(available_backends[1]),
        (
            f" (sub-backend {int(fused_attention_backend)})"
            if fused_attention_backend is not None
            else ""
        ),
        bool(available_backends[2]),
    )
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    # Select FusedAttention for performance
    if (
        use_flash_attention
        and use_fused_attention
        and fused_attention_backend == FusedAttnBackend["F16_arbitrary_seqlen"]
    ):
        if device_compute_capability == (9, 0):
            logger.debug(
                "Disabling FlashAttention to give FusedAttention preference on Hopper+ "
                "for performance reasons"
            )
            use_flash_attention = False

    # 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
    to efficienly calculate and store the context during inference.

    Parameters
    ----------
    max_batch_size : int
                    maximum batch size during inference.
    max_sequence_length : int
                         maximum sequence length during inference.
    """

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

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

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

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

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

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


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@torch.no_grad()
def get_alibi(
    num_heads: int,
    max_seqlen_q: int,
    max_seqlen_kv: int,
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    alibi_slopes: Optional[torch.Tensor] = None,
    bias_dtype: Optional[torch.dtype] = None,
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    bottom_right_alignment: bool = True,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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    """
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    Parameters
    ----------
    num_heads: int
        Number of heads.
    max_seqlen_q: int
        Maximum sequence length for queries.
    max_seqlen_kv: int
        Maximum sequence length for keys and values.
    alibi_slopes: Optional[torch.Tensor], default = `None`
        Custom ALiBi slopes, FP32, CUDA tensor, in shape [num_heads] or [batch_size, num_heads].
    bias_dtype: Optional[torch.dtype], default = `None`
        Dtype of the generated ALiBi bias. If None, use torch.float32.
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    bottom_right_alignment: bool, default = `True`
        Whether to align the diagonal of the ALiBi bias to the bottom right corner of
        the matrix (`True`) or top left (`False`).
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    Returns
    ----------
    alibi_slopes: torch.Tensor
        ALiBi slopes in FP32 and shape [num_heads] or [batch_size, num_heads].
    alibi_bias: torch.Tensor
        ALiBi bias in FP32 or `bias_dtype`. If `alibi_slopes` is in [num_heads] shape,
        then `alibi_bias` is in [1, num_heads, max_seqlen_q, max_seqlen_kv], and if
        `alibi_slopes` is in [batch_size, num_heads], then the bias is in
        [batch_size, num_heads, max_seqlen_q, max_seqlen_kv].
    """
    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])
        if _alibi_cache["_alibi_slopes"].dim() == 2:
            slopes_shape = torch.Size([*_alibi_cache["_alibi_slopes"].shape[:], 1, 1])
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        if bottom_right_alignment:
            bias = torch.arange(1 - max_seqlen_kv, 1, dtype=torch.int32, device="cuda").view(
                1, 1, 1, max_seqlen_kv
            )
        else:
            bias = torch.arange(
                1 - max_seqlen_q, max_seqlen_kv - max_seqlen_q + 1, dtype=torch.int32, device="cuda"
            ).view(1, 1, 1, max_seqlen_kv)
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        bias = bias - torch.arange(1 - max_seqlen_q, 1, dtype=torch.int32, device="cuda").view(
            1, 1, max_seqlen_q, 1
        )
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        bias = bias.abs().mul(-1)
        bias = bias * _alibi_cache["_alibi_slopes"].view(slopes_shape)
        _alibi_cache["_max_seqlen_q"], _alibi_cache["_max_seqlen_kv"] = max_seqlen_q, max_seqlen_kv
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        _alibi_cache["_bottom_right_alignment"] = bottom_right_alignment
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        bias_dtype = torch.float32 if bias_dtype is None else bias_dtype
        _alibi_cache["_alibi_bias"] = bias.contiguous().to(dtype=bias_dtype, device="cuda")
        _alibi_cache["_alibi_bias_require_update"] = False

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

    return cu_seqlens

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

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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))

    mask = mask.reshape(-1)
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    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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    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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@jit_fuser
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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    tensor = torch.cat((tensor, padding_indice), dim=0)

    indices = indices.repeat(1, tensor.shape[1], tensor.shape[2])
    packed = torch.gather(tensor, 0, indices)
    return packed


@jit_fuser
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


@jit_fuser
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


@jit_fuser
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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    unpacked.scatter_(0, indices, tensor)
1128
    unpacked = unpacked[0:-1, :, :]
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    return unpacked


@jit_fuser
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


@jit_fuser
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, ...]
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    ) -> Union[Tuple[torch.Tensor, ...], torch.Tensor]:
        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, ...]):
1184
        (indices,) = ctx.saved_tensors
1185
        if len(grad_outputs) == 1:
1186
            return None, unpack_tensor(indices, ctx.dim0, *grad_outputs)
1187
        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:
1204
        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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        (indices,) = ctx.saved_tensors
        return None, None, pack_tensor(indices, grad_output)
1211
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def flash_attn_p2p_communicate(
    rank, send_tensor, send_dst, recv_tensor, recv_src, cp_group, batch_p2p_comm
):
1216
    """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


1255
@jit_fuser
1256
def flash_attn_fwd_out_correction(out, out_per_step, seq_dim, softmax_lse, softmax_lse_per_step):
1257
    """Merge partial outputs of each step in Attention with context parallelism"""
1258
    softmax_lse_corrected_exp = torch.exp(softmax_lse_per_step - softmax_lse).movedim(2, seq_dim)
1259
    softmax_lse_corrected_exp = softmax_lse_corrected_exp.unsqueeze(-1)
1260
    out_corrected = out_per_step * softmax_lse_corrected_exp
1261
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1263
    out.add_(out_corrected)


1264
@jit_fuser
1265
def flash_attn_fwd_softmax_lse_correction(softmax_lse, softmax_lse_per_step):
1266
    """Merge softmax stats of each step in Attention with context parallelism"""
1267
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    max_scale = torch.max(softmax_lse, softmax_lse_per_step)
    min_scale = torch.min(softmax_lse, softmax_lse_per_step)
    new_scale = max_scale + torch.log(1 + torch.exp(min_scale - max_scale))
    softmax_lse.copy_(new_scale)
1271
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@jit_fuser
def get_cu_seqlens_on_cp_rank(
    cu_seqlens, cu_seqlens_padded_on_cp_rank, cp_size, cp_rank, first_half, second_half
):
    """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


1294
class AttnFuncWithCPAndKVP2P(torch.autograd.Function):
1295
    """
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    Attention implementation with context parallelism. Exchange KV between CP ranks
    with P2P in ring topology. Split attention compute into multiple steps, and overlap
    current-step compute with next-step communication.
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    """

    @staticmethod
1302
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1308
    def forward(
        ctx,
        is_training,
        q,
        k,
        v,
        cu_seqlens_q,
1309
        cu_seqlens_kv,
1310
        max_seqlen_q,
1311
        max_seqlen_kv,
1312
1313
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
1314
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1322
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1325
        dropout_p,
        cp_group,
        cp_global_ranks,
        cp_stream,
        softmax_scale,
        qkv_format,
        attn_mask_type,
        attn_bias_type,
        attn_bias,
        deterministic,
        use_fused_attention,
    ):
1326
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1331
        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)
        send_dst = cp_global_ranks[(rank + 1) % cp_size]
1332
        recv_src = cp_global_ranks[(rank - 1) % cp_size]
1333
1334
        batch_p2p_comm = int(os.getenv("NVTE_BATCH_MHA_P2P_COMM", "0")) or (cp_size == 2)

1335
1336
        causal = "causal" in attn_mask_type
        padding = "padding" in attn_mask_type
1337

1338
        if qkv_format in ["bshd", "sbhd"]:
1339
            seq_dim = qkv_format.index("s")
1340
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            qkv_layout = qkv_format + "_" + qkv_format[:-2] + "2" + qkv_format[-2:]
        else:
            qkv_layout = qkv_format + "_" + qkv_format + "_" + qkv_format

        pad_between_seqs_q = not torch.equal(cu_seqlens_q_padded, cu_seqlens_q)
        pad_between_seqs_kv = not torch.equal(cu_seqlens_kv_padded, cu_seqlens_kv)
        max_seqlen_q = max_seqlen_q // cp_size
        max_seqlen_kv = max_seqlen_kv // cp_size
        cu_seqlens_q_padded = cu_seqlens_q_padded // cp_size
        cu_seqlens_kv_padded = cu_seqlens_kv_padded // cp_size
        cu_seqlens_q_per_step = [None for _ in range(cp_size)]
        cu_seqlens_kv_per_step = [None for _ in range(cp_size)]
1352

1353
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1355
        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!"
1356
        if causal:
1357
1358
            if qkv_format == "bshd":
                # [b, s, np, hn] -> [b, 2, s//2, np, hn]
1359
                q, k, v = [x.view(x.shape[0], 2, x.shape[1] // 2, *x.shape[2:]) for x in [q, k, v]]
1360
1361
            elif qkv_format == "sbhd":
                # [s, b, np, hn] -> [2, s//2, b, np, hn]
1362
                q, k, v = [x.view(2, x.shape[0] // 2, *x.shape[1:]) for x in [q, k, v]]
1363
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1365
        total_tokens_kv = None if qkv_format != "thd" else k.shape[0]
        # remove padded tokens at the end
        k, v = [x if qkv_format != "thd" else x[: cu_seqlens_kv_padded[-1]] for x in [k, v]]
1366
        if attn_bias is not None:
1367
            assert len(attn_bias.shape) == 4, (
1368
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1370
                "Only support bias shape of [b, h, sq, sk] for forward, "
                "and [1, h, sq, sk] for backward!"
            )
1371
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1373
            assert (
                attn_bias.shape[-2] % 2 == 0 and attn_bias.shape[-1] % (2 * cp_size) == 0
            ), "Sequence length does not meet divisible requirements!"
1374
            # [b, np, sq, sk] -> [b, np, 2, sq//2, 2*cp, sk//(2*cp)]
1375
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            attn_bias_ = attn_bias.view(
                *attn_bias.shape[:-2],
                2,
                attn_bias.shape[-2] // 2,
                2 * cp_size,
                attn_bias.shape[-1] // (2 * cp_size),
1381
1382
            )
            # [b, np, sq, sk] -> [b, np, sq, 2*cp, sk//(2*cp)]
1383
1384
            attn_bias = attn_bias.view(
                *attn_bias.shape[:-1], 2 * cp_size, attn_bias.shape[-1] // (2 * cp_size)
1385
            )
1386
        assert q.shape[-1] % 8 == 0, "hidden size per attention head should be multiple of 8"
1387
1388
        fa_optional_forward_kwargs = {}
        if _flash_attn_2_3_plus:
1389
            fa_optional_forward_kwargs["window_size"] = (-1, 0) if causal else (-1, -1)
1390
1391
        if _flash_attn_2_4_plus:
            fa_optional_forward_kwargs["alibi_slopes"] = None
1392
1393
        if _flash_attn_2_5_7_plus:
            fa_optional_forward_kwargs["block_table"] = None
1394

1395
1396
1397
        # Flash Attn inputs
        q_inputs = [None, None]
        kv_inputs = [None, None]
1398
        attn_bias_inputs = [None, None]
1399
1400
1401
1402
        # 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)]
1403
        attn_biases = [None for _ in range(cp_size)]
1404
1405
1406
1407
1408
1409
1410

        # 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)]
1411
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1414
        if use_fused_attention and qkv_format in ["bshd", "sbhd"]:
            p2p_comm_buffers[0] = torch.cat((k.unsqueeze(-3), v.unsqueeze(-3)), dim=-3)
        else:
            p2p_comm_buffers[0] = torch.cat((k.unsqueeze(0), v.unsqueeze(0)), dim=0)
1415
1416
        send_recv_reqs = [[], []]

1417
        for i in range(cp_size + 1):
1418
            if i < cp_size:
1419
                with torch.cuda.stream(flash_attn_streams[i % 2]):
1420
                    # wait until KV is received
1421
                    for req in send_recv_reqs[(i + 1) % 2]:
1422
1423
                        req.wait()

1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
                    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,
                        )

                    kv_inputs[i % 2] = p2p_comm_buffers[i]
1437
1438
                    if causal:
                        if i == 0:
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
                            if pad_between_seqs_q:
                                cu_seqlens_q_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_q, cu_seqlens_q_padded, cp_size, rank, True, True
                                )
                            else:
                                cu_seqlens_q_per_step[i] = cu_seqlens_q // cp_size
                            if pad_between_seqs_kv:
                                cu_seqlens_kv_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_kv, cu_seqlens_kv_padded, cp_size, rank, True, True
                                )
                            else:
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
1451
                            if use_fused_attention:
1452
1453
                                if qkv_format == "bshd":
                                    # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
1454
                                    q_inputs[i % 2] = q.view(q.shape[0], -1, *q.shape[-2:])
1455
                                    # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
1456
                                    kv_inputs[i % 2] = kv_inputs[i % 2].view(
1457
                                        k.shape[0], -1, 2, *k.shape[-2:]
1458
                                    )
1459
1460
                                elif qkv_format == "sbhd":
                                    # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
1461
                                    q_inputs[i % 2] = q.view(-1, *q.shape[-3:])
1462
1463
1464
1465
                                    # [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:]
                                    )
1466
                                elif qkv_format == "thd":
1467
                                    q_inputs[i % 2] = q
1468
1469
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
1470
1471
1472
1473
1474
1475
                                    attn_bias_inputs[i % 2] = torch.cat(
                                        (
                                            attn_bias[..., idx, :],
                                            attn_bias[..., (2 * cp_size - idx - 1), :],
                                        ),
                                        dim=-1,
1476
                                    ).contiguous()
1477
1478
1479
1480
                                out_per_step[i], [softmax_lse_per_step[i], rng_states[i], *rest] = (
                                    fused_attn_fwd(
                                        is_training,
                                        max_seqlen_q,
1481
1482
1483
                                        max_seqlen_kv,
                                        cu_seqlens_q_per_step[i],
                                        cu_seqlens_kv_per_step[i],
1484
                                        q_inputs[i % 2],
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
                                        (
                                            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]
                                        ),
1495
1496
1497
1498
1499
1500
1501
1502
                                        TE_DType[q.dtype],
                                        tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                                        attn_scale=softmax_scale,
                                        dropout=dropout_p,
                                        qkv_layout=qkv_layout,
                                        attn_mask_type=attn_mask_type,
                                        attn_bias_type=attn_bias_type,
                                        attn_bias=attn_bias_inputs[i % 2],
1503
1504
                                        cu_seqlens_q_padded=cu_seqlens_q_padded,
                                        cu_seqlens_kv_padded=cu_seqlens_kv_padded,
1505
                                    )
1506
                                )
1507
1508
                                if len(rest) > 0:
                                    attn_biases[i] = rest[0]
1509
1510
                            else:
                                # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
1511
                                q_inputs[i % 2] = q.view(-1, *q.shape[-2:])
1512
                                # [2, b, 2, sk//2, np, hn] -> [2, b*sk, np, hn]
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
                                kv_inputs[i % 2] = kv_inputs[i % 2].view(2, -1, *k.shape[-2:])
                                (
                                    _,
                                    _,
                                    _,
                                    _,
                                    out_per_step[i],
                                    softmax_lse_per_step[i],
                                    _,
                                    rng_states[i],
                                ) = _flash_attn_forward(
                                    q_inputs[i % 2],
                                    kv_inputs[i % 2][0],
                                    kv_inputs[i % 2][1],
1527
1528
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
1529
                                    max_seqlen_q,
1530
                                    max_seqlen_kv,
1531
1532
1533
1534
1535
                                    dropout_p,
                                    softmax_scale,
                                    causal=True,
                                    return_softmax=False,
                                    **fa_optional_forward_kwargs,
1536
                                )
1537
                        elif i <= rank:
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
                            if pad_between_seqs_q:
                                cu_seqlens_q_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_q, cu_seqlens_q_padded, cp_size, rank, True, True
                                )
                            else:
                                cu_seqlens_q_per_step[i] = cu_seqlens_q // cp_size
                            if pad_between_seqs_kv:
                                cu_seqlens_kv_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_kv,
                                    cu_seqlens_kv_padded,
                                    cp_size,
                                    (rank - i) % cp_size,
                                    True,
                                    False,
                                )
                            else:
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // (cp_size * 2)
1555
                            if use_fused_attention:
1556
1557
                                if qkv_format == "bshd":
                                    # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
1558
                                    q_inputs[i % 2] = q.view(q.shape[0], -1, *q.shape[-2:])
1559
1560
                                    # [b, 2, sk//2, 2, np, hn] -> [b, sk//2, 2, np, hn]
                                    kv_inputs[i % 2] = kv_inputs[i % 2][:, 0, ...].contiguous()
1561
1562
                                elif qkv_format == "sbhd":
                                    # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
1563
                                    q_inputs[i % 2] = q.view(-1, *q.shape[-3:])
1564
1565
                                    # [2, sk//2, b, 2, np, hn] -> [sk//2, b, 2, np, hn]
                                    kv_inputs[i % 2] = kv_inputs[i % 2][0].contiguous()
1566
                                elif qkv_format == "thd":
1567
                                    q_inputs[i % 2] = q
1568
                                    # [2, t, np, hn] -> [2, t/2, np, hn]
1569
                                    kv_inputs[i % 2] = tex.thd_read_half_tensor(
1570
                                        kv_inputs[i % 2], cu_seqlens_kv_padded, 0
1571
                                    )
1572
1573
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
1574
1575
1576
1577
1578
                                    attn_bias_inputs[i % 2] = attn_bias[..., idx, :].contiguous()
                                out_per_step[i], [softmax_lse_per_step[i], rng_states[i], *rest] = (
                                    fused_attn_fwd(
                                        is_training,
                                        max_seqlen_q,
1579
1580
1581
                                        max_seqlen_kv // 2,
                                        cu_seqlens_q_per_step[i],
                                        cu_seqlens_kv_per_step[i],
1582
                                        q_inputs[i % 2],
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
                                        (
                                            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]
                                        ),
1593
1594
1595
1596
1597
1598
1599
1600
                                        TE_DType[q.dtype],
                                        tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                                        attn_scale=softmax_scale,
                                        dropout=dropout_p,
                                        qkv_layout=qkv_layout,
                                        attn_mask_type="padding" if padding else "no_mask",
                                        attn_bias_type=attn_bias_type,
                                        attn_bias=attn_bias_inputs[i % 2],
1601
1602
1603
1604
1605
                                        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
1606
1607
                                        ),
                                    )
1608
                                )
1609
1610
                                if len(rest) > 0:
                                    attn_biases[i] = rest[0]
1611
1612
                            else:
                                # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
1613
                                q_inputs[i % 2] = q.view(-1, *q.shape[-2:])
1614
1615
                                if qkv_format == "thd":
                                    # [2, t, np, hn] -> [2, t/2, np, hn]
1616
                                    kv_inputs[i % 2] = tex.thd_read_half_tensor(
1617
                                        kv_inputs[i % 2], cu_seqlens_kv_padded, 0
1618
                                    )
1619
1620
                                else:
                                    # [2, b, 2, sk//2, np, hn] -> [2, b, sk//2, np, hn]
1621
                                    kv_inputs[i % 2] = kv_inputs[i % 2][:, :, 0, ...].contiguous()
1622
                                # [2, b, sk//2, np, hn] -> [2, b*sk//2, np, hn]
1623
                                kv_inputs[i % 2] = kv_inputs[i % 2].view(2, -1, *k.shape[-2:])
1624
                                if _flash_attn_2_3_plus:
1625
                                    fa_optional_forward_kwargs["window_size"] = (-1, -1)
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
                                (
                                    _,
                                    _,
                                    _,
                                    _,
                                    out_per_step[i],
                                    softmax_lse_per_step[i],
                                    _,
                                    rng_states[i],
                                ) = _flash_attn_forward(
                                    q_inputs[i % 2],
                                    kv_inputs[i % 2][0],
                                    kv_inputs[i % 2][1],
1639
1640
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
1641
                                    max_seqlen_q,
1642
                                    max_seqlen_kv // 2,
1643
1644
1645
1646
1647
                                    dropout_p,
                                    softmax_scale,
                                    causal=False,
                                    return_softmax=False,
                                    **fa_optional_forward_kwargs,
1648
1649
                                )
                        else:
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
                            if pad_between_seqs_q:
                                cu_seqlens_q_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_q, cu_seqlens_q_padded, cp_size, rank, False, True
                                )
                            else:
                                cu_seqlens_q_per_step[i] = cu_seqlens_q // (cp_size * 2)
                            if pad_between_seqs_kv:
                                cu_seqlens_kv_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_kv,
                                    cu_seqlens_kv_padded,
                                    cp_size,
                                    (rank - i) % cp_size,
                                    True,
                                    True,
                                )
                            else:
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
1667
                            if use_fused_attention:
1668
1669
                                if qkv_format == "bshd":
                                    # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
1670
                                    q_inputs[i % 2] = q[:, 1, ...].contiguous()
1671
                                    # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
1672
                                    kv_inputs[i % 2] = kv_inputs[i % 2].view(
1673
                                        k.shape[0], -1, 2, *k.shape[-2:]
1674
                                    )
1675
1676
                                elif qkv_format == "sbhd":
                                    # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
1677
                                    q_inputs[i % 2] = q[1].contiguous()
1678
1679
1680
1681
                                    # [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:]
                                    )
1682
1683
                                elif qkv_format == "thd":
                                    # [t, np, hn] -> [t/2, np, hn]
1684
1685
1686
                                    q_inputs[i % 2] = tex.thd_read_half_tensor(
                                        q, cu_seqlens_q_padded, 1
                                    )
1687
1688
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
1689
1690
1691
1692
1693
1694
                                    attn_bias_inputs[i % 2] = torch.cat(
                                        (
                                            attn_bias_[..., 1, :, idx, :],
                                            attn_bias_[..., 1, :, (2 * cp_size - idx - 1), :],
                                        ),
                                        dim=-1,
1695
                                    ).contiguous()
1696
1697
1698
1699
                                out_per_step[i], [softmax_lse_per_step[i], rng_states[i], *rest] = (
                                    fused_attn_fwd(
                                        is_training,
                                        max_seqlen_q // 2,
1700
1701
1702
                                        max_seqlen_kv,
                                        cu_seqlens_q_per_step[i],
                                        cu_seqlens_kv_per_step[i],
1703
                                        q_inputs[i % 2],
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
                                        (
                                            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]
                                        ),
1714
1715
1716
1717
1718
1719
1720
1721
                                        TE_DType[q.dtype],
                                        tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                                        attn_scale=softmax_scale,
                                        dropout=dropout_p,
                                        qkv_layout=qkv_layout,
                                        attn_mask_type="padding" if padding else "no_mask",
                                        attn_bias_type=attn_bias_type,
                                        attn_bias=attn_bias_inputs[i % 2],
1722
1723
1724
1725
                                        cu_seqlens_q_padded=(
                                            None
                                            if cu_seqlens_q_padded is None
                                            else cu_seqlens_q_padded // 2
1726
                                        ),
1727
                                        cu_seqlens_kv_padded=cu_seqlens_kv_padded,
1728
                                    )
1729
                                )
1730
1731
                                if len(rest) > 0:
                                    attn_biases[i] = rest[0]
1732
                            else:
1733
1734
                                if qkv_format == "thd":
                                    # [t, np, hn] -> [t/2, np, hn]
1735
1736
1737
                                    q_inputs[i % 2] = tex.thd_read_half_tensor(
                                        q, cu_seqlens_q_padded, 1
                                    )
1738
1739
                                else:
                                    # [b, 2, sq//2, np, hn]->[b, sq//2, np, hn]->[b*sq//2, np, hn]
1740
                                    q_inputs[i % 2] = (
1741
                                        q[:, 1, ...].contiguous().view(-1, *q.shape[-2:])
1742
                                    )
1743
                                # [2, b, 2, sk//2, np, hn] -> [2, b*sk, np, hn]
1744
                                kv_inputs[i % 2] = kv_inputs[i % 2].view(2, -1, *k.shape[-2:])
1745
                                if _flash_attn_2_3_plus:
1746
                                    fa_optional_forward_kwargs["window_size"] = (-1, -1)
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
                                (
                                    _,
                                    _,
                                    _,
                                    _,
                                    out_per_step[i],
                                    softmax_lse_per_step[i],
                                    _,
                                    rng_states[i],
                                ) = _flash_attn_forward(
                                    q_inputs[i % 2],
                                    kv_inputs[i % 2][0],
                                    kv_inputs[i % 2][1],
1760
1761
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
1762
                                    max_seqlen_q // 2,
1763
                                    max_seqlen_kv,
1764
1765
1766
1767
1768
                                    dropout_p,
                                    softmax_scale,
                                    causal=False,
                                    return_softmax=False,
                                    **fa_optional_forward_kwargs,
1769
1770
                                )
                    else:
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
                        if pad_between_seqs_q:
                            cu_seqlens_q_per_step[i] = get_cu_seqlens_on_cp_rank(
                                cu_seqlens_q, cu_seqlens_q_padded, cp_size, rank, True, True
                            )
                        else:
                            cu_seqlens_q_per_step[i] = cu_seqlens_q // cp_size
                        if pad_between_seqs_kv:
                            cu_seqlens_kv_per_step[i] = get_cu_seqlens_on_cp_rank(
                                cu_seqlens_kv,
                                cu_seqlens_kv_padded,
                                cp_size,
                                (rank - i) % cp_size,
                                True,
                                True,
                            )
                        else:
                            cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
1788
                        if use_fused_attention:
1789
1790
                            if attn_bias is not None:
                                idx = (rank - i) % cp_size
1791
1792
1793
1794
1795
1796
                                attn_bias_inputs[i % 2] = torch.cat(
                                    (
                                        attn_bias[..., idx, :],
                                        attn_bias[..., (2 * cp_size - idx - 1), :],
                                    ),
                                    dim=-1,
1797
                                ).contiguous()
1798
1799
1800
1801
                            out_per_step[i], [softmax_lse_per_step[i], rng_states[i], *rest] = (
                                fused_attn_fwd(
                                    is_training,
                                    max_seqlen_q,
1802
1803
1804
                                    max_seqlen_kv,
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
1805
                                    q,
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
                                    (
                                        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]
                                    ),
1816
1817
1818
1819
1820
1821
1822
1823
                                    TE_DType[q.dtype],
                                    tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                                    attn_scale=softmax_scale,
                                    dropout=dropout_p,
                                    qkv_layout=qkv_layout,
                                    attn_mask_type=attn_mask_type,
                                    attn_bias_type=attn_bias_type,
                                    attn_bias=attn_bias_inputs[i % 2],
1824
1825
                                    cu_seqlens_q_padded=cu_seqlens_q_padded,
                                    cu_seqlens_kv_padded=cu_seqlens_kv_padded,
1826
                                )
1827
                            )
1828
1829
                            if len(rest) > 0:
                                attn_biases[i] = rest[0]
1830
                        else:
1831
                            # [b, sq, np, hn] -> [b*sq, np, hn]
1832
                            q_inputs[i % 2] = q.view(-1, *q.shape[-2:])
1833
                            # [2, b, sk, np, hn] -> [2, b*sk, np, hn]
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
                            kv_inputs[i % 2] = kv_inputs[i % 2].view(2, -1, *k.shape[-2:])
                            (
                                _,
                                _,
                                _,
                                _,
                                out_per_step[i],
                                softmax_lse_per_step[i],
                                _,
                                rng_states[i],
                            ) = _flash_attn_forward(
                                q_inputs[i % 2],
                                kv_inputs[i % 2][0],
                                kv_inputs[i % 2][1],
1848
1849
                                cu_seqlens_q_per_step[i],
                                cu_seqlens_kv_per_step[i],
1850
                                max_seqlen_q,
1851
                                max_seqlen_kv,
1852
1853
1854
1855
1856
                                dropout_p,
                                softmax_scale,
                                causal=False,
                                return_softmax=False,
                                **fa_optional_forward_kwargs,
1857
                            )
1858
1859
1860
1861

            if i > 0:
                # wait until fwd restuls correction of last step is done
                if i > 1:
1862
                    flash_attn_streams[(i - 1) % 2].wait_event(fwd_results_correction_done)
1863

1864
1865
                if use_fused_attention:
                    # [b, np, sq, 1] -> [b, np, sq]
1866
                    softmax_lse_per_step[i - 1].squeeze_(-1)
1867

1868
                with torch.cuda.stream(flash_attn_streams[(i - 1) % 2]):
1869
                    if i == 1:
1870
                        out = torch.zeros_like(q)
1871
                        softmax_lse = torch.clone(softmax_lse_per_step[0]).to(torch.double)
1872
                        if causal and qkv_format != "thd":
1873
1874
                            # [b, np, sq] -> [b, np, 2, sq//2]
                            softmax_lse_ = softmax_lse.view(
1875
                                *softmax_lse.shape[:-1], 2, softmax_lse.shape[-1] // 2
1876
                            )
1877
1878
1879
1880
                    elif (i - 1) <= rank or not causal:
                        flash_attn_fwd_softmax_lse_correction(
                            softmax_lse, softmax_lse_per_step[i - 1]
                        )
1881
                    else:
1882
                        if qkv_format == "thd":
1883
                            tex.thd_second_half_lse_correction(
1884
1885
1886
1887
                                softmax_lse,
                                softmax_lse_per_step[i - 1],
                                cu_seqlens_q_padded,
                                max_seqlen_q,
1888
                            )
1889
                        else:
1890
1891
1892
                            flash_attn_fwd_softmax_lse_correction(
                                softmax_lse_[..., 1, :], softmax_lse_per_step[i - 1]
                            )
1893
1894

                if i < cp_size:
1895
                    flash_attn_streams[(i - 1) % 2].record_event(fwd_results_correction_done)
1896
1897
1898
1899
1900

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

        softmax_lse = softmax_lse.to(torch.float)
        for i in range(cp_size):
1901
1902
1903
1904
1905
1906
            if qkv_format == "bshd":
                out_per_step[i] = out_per_step[i].view(out.shape[0], -1, *out.shape[-2:])
                out_ = out[:, 1, ...]
            elif qkv_format == "sbhd":
                out_per_step[i] = out_per_step[i].view(-1, *out.shape[-3:])
                out_ = out[1]
1907

1908
            if i <= rank or not causal:
1909
                if qkv_format in ["bshd", "sbhd"]:
1910
1911
1912
1913
1914
1915
1916
                    flash_attn_fwd_out_correction(
                        out.view(*out_per_step[i].shape),
                        out_per_step[i],
                        seq_dim,
                        softmax_lse,
                        softmax_lse_per_step[i],
                    )
1917
                elif qkv_format == "thd":
1918
1919
1920
1921
1922
                    tex.thd_out_correction(
                        out,
                        out_per_step[i],
                        softmax_lse,
                        softmax_lse_per_step[i],
1923
                        cu_seqlens_q_padded,
1924
1925
                        False,
                    )
1926
            else:
1927
                if qkv_format in ["bshd", "sbhd"]:
1928
1929
1930
1931
1932
1933
1934
                    flash_attn_fwd_out_correction(
                        out_,
                        out_per_step[i],
                        seq_dim,
                        softmax_lse_[..., 1, :],
                        softmax_lse_per_step[i],
                    )
1935
                elif qkv_format == "thd":
1936
1937
1938
1939
1940
                    tex.thd_out_correction(
                        out,
                        out_per_step[i],
                        softmax_lse,
                        softmax_lse_per_step[i],
1941
                        cu_seqlens_q_padded,
1942
1943
                        True,
                    )
1944
1945

        kv = p2p_comm_buffers[-1]
1946
        if use_fused_attention:
1947
1948
1949
1950
            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:])
1951
1952
        else:
            out = out.view(-1, *out.shape[-2:])
1953

1954
        ctx.save_for_backward(
1955
1956
1957
1958
            q,
            kv,
            out,
            softmax_lse,
1959
1960
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
1961
1962
            *cu_seqlens_q_per_step,
            *cu_seqlens_kv_per_step,
1963
1964
            *rng_states,
            *attn_biases,
1965
        )
1966
1967
1968
        ctx.cp_group = cp_group
        ctx.cp_global_ranks = cp_global_ranks
        ctx.dropout_p = dropout_p
1969
        ctx.total_tokens_kv = total_tokens_kv
1970
        ctx.max_seqlen_q = max_seqlen_q
1971
        ctx.max_seqlen_kv = max_seqlen_kv
1972
        ctx.softmax_scale = softmax_scale
1973
        ctx.qkv_format = qkv_format
1974
        ctx.attn_mask_type = attn_mask_type
1975
1976
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_bias_shape = None if attn_bias is None else attn_bias.shape
1977
        ctx.deterministic = deterministic
1978
        ctx.use_fused_attention = use_fused_attention
1979
1980
1981
1982
1983
1984
        return out

    @staticmethod
    def backward(ctx, dout):
        cp_size = get_distributed_world_size(ctx.cp_group)
        rank = get_distributed_rank(ctx.cp_group)
1985
        send_dst = ctx.cp_global_ranks[(rank - 1) % cp_size]
1986
1987
1988
        recv_src = ctx.cp_global_ranks[(rank + 1) % cp_size]
        batch_p2p_comm = int(os.getenv("NVTE_BATCH_MHA_P2P_COMM", "0")) or (cp_size == 2)

1989
1990
1991
1992
1993
1994
        (q, kv, out, softmax_lse, cu_seqlens_q_padded, cu_seqlens_kv_padded) = ctx.saved_tensors[:6]
        cu_seqlens_q_per_step = ctx.saved_tensors[6 : 6 + cp_size]
        cu_seqlens_kv_per_step = ctx.saved_tensors[6 + cp_size : 6 + cp_size * 2]
        rng_states = ctx.saved_tensors[6 + cp_size * 2 : 6 + cp_size * 3]
        attn_biases = ctx.saved_tensors[6 + cp_size * 3 : 6 + cp_size * 4]

1995
1996
        causal = "causal" in ctx.attn_mask_type
        padding = "padding" in ctx.attn_mask_type
1997
1998
1999
2000
        if ctx.qkv_format in ["bshd", "sbhd"]:
            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
2001

2002
        if attn_biases[0] is not None:
2003
2004
            # [b, np, sq, 2*cp, sk//(2*cp)]
            attn_dbias = torch.zeros(
2005
                *ctx.attn_bias_shape, dtype=attn_biases[0].dtype, device=attn_biases[0].device
2006
2007
2008
            )
            # [b, np, sq, 2*cp, sk//(2*cp)] -> [b, np, 2, sq//2, 2*cp, sk//(2*cp)]
            attn_dbias_ = attn_dbias.view(
2009
                *attn_dbias.shape[:-3], 2, attn_dbias.shape[-3] // 2, *attn_dbias.shape[-2:]
2010
2011
2012
2013
            )
        else:
            attn_dbias = None

2014
        if causal:
2015
            if ctx.qkv_format == "thd":
2016
2017
2018
                softmax_lse_ = tex.thd_read_second_half_lse(
                    softmax_lse, cu_seqlens_q_padded, ctx.max_seqlen_q
                )
2019
2020
            else:
                # [b, np, sq] -> [b, np, 2, sq//2]
2021
2022
2023
                softmax_lse_ = softmax_lse.view(
                    *softmax_lse.shape[:-1], 2, softmax_lse.shape[-1] // 2
                )
2024
2025
2026
2027
2028
                softmax_lse_ = softmax_lse_[..., 1, :].contiguous()
                if ctx.use_fused_attention:
                    # [b, np, sq//2] -> [b, np, sq//2, 1]
                    softmax_lse_.unsqueeze_(-1)

2029
2030
2031
        if ctx.use_fused_attention:
            # [b, np, sq] -> [b, np, sq, 1]
            softmax_lse.unsqueeze_(-1)
2032
2033
2034
2035
        out = out.view(*q.shape)
        dout = dout.view(*q.shape)
        # Flash Attn outputs
        dq = torch.empty_like(q)
2036
2037
        if ctx.qkv_format == "thd" and causal:
            dq[cu_seqlens_q_padded[-1] :].fill_(0)
2038

2039
2040
2041
2042
        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),
        ]
2043
2044
2045
        p2p_comm_buffers[0][0].copy_(kv)
        send_recv_reqs = []

2046
2047
2048
2049
2050
2051
        fa_optional_backward_kwargs = {}
        if _flash_attn_2_4_plus:
            fa_optional_backward_kwargs["alibi_slopes"] = None
        if _flash_attn_2_4_1_plus:
            fa_optional_backward_kwargs["deterministic"] = ctx.deterministic

2052
2053
2054
2055
2056
        for i in range(cp_size):
            # wait until KV is received
            for req in send_recv_reqs:
                req.wait()

2057
2058
            send_tensor = p2p_comm_buffers[i % 2]
            recv_tensor = p2p_comm_buffers[(i + 1) % 2]
2059
2060
2061
            if i == 0:
                send_tensor = send_tensor[0]
                recv_tensor = recv_tensor[0]
2062
            if i == (cp_size - 1):
2063
2064
2065
                send_tensor = send_tensor[1]
                recv_tensor = recv_tensor[1]

2066
2067
2068
            send_recv_reqs = flash_attn_p2p_communicate(
                rank, send_tensor, send_dst, recv_tensor, recv_src, ctx.cp_group, batch_p2p_comm
            )
2069

2070
            kv = p2p_comm_buffers[i % 2][0]
2071
            # In reversed order of fwd
2072
            if causal:
2073
                if i == (cp_size - 1):
2074
                    if ctx.use_fused_attention:
2075
2076
2077
                        if ctx.qkv_format == "bshd":
                            # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                            q_ = q.view(q.shape[0], -1, *q.shape[-2:])
2078
2079
                            # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
                            kv_ = kv.view(kv.shape[0], -1, *kv.shape[-3:])
2080
2081
2082
2083
2084
2085
                            # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                            out_ = out.view(out.shape[0], -1, *out.shape[-2:])
                            dout_ = dout.view(dout.shape[0], -1, *dout.shape[-2:])
                        elif ctx.qkv_format == "sbhd":
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            q_ = q.view(-1, *q.shape[-3:])
2086
2087
                            # [2, sk//2, b, 2, np, hn] -> [sk, b, 2, np, hn]
                            kv_ = kv.view(-1, *kv.shape[-4:])
2088
2089
2090
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            out_ = out.view(-1, *out.shape[-3:])
                            dout_ = dout.view(-1, *dout.shape[-3:])
2091
2092
                        elif ctx.qkv_format == "thd":
                            q_, kv_, out_, dout_ = q, kv, out, dout
2093
                        aux_ctx_tensors = [softmax_lse, rng_states[cp_size - i - 1]]
2094
                        if attn_dbias is not None:
2095
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2096
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2097
                            ctx.max_seqlen_q,
2098
2099
2100
                            ctx.max_seqlen_kv,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2101
                            q_,
2102
2103
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
2104
2105
2106
2107
2108
                            out_,
                            dout_,
                            TE_DType[q.dtype],
                            TE_DType[kv.dtype],
                            aux_ctx_tensors,
2109
                            tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
2110
2111
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
                            cu_seqlens_kv_padded=cu_seqlens_kv_padded,
2112
2113
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
2114
                            qkv_layout=qkv_layout,
2115
                            attn_mask_type=ctx.attn_mask_type,
2116
                            attn_bias_type=ctx.attn_bias_type,
2117
2118
2119
2120
                        )
                    else:
                        # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                        q_ = q.view(-1, *q.shape[-2:])
2121
                        dq_ = torch.zeros_like(q_)
2122
2123
2124
2125
2126
2127
2128
                        # [2, b, 2, sk//2, np, hn] -> [2, b*sk, np, hn]
                        kv_ = kv.view(2, -1, *kv.shape[-2:])
                        dkv_ = torch.empty_like(kv_)
                        # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                        out_ = out.view(-1, *out.shape[-2:])
                        dout_ = dout.view(-1, *dout.shape[-2:])
                        if _flash_attn_2_3_plus:
2129
                            fa_optional_backward_kwargs["window_size"] = (-1, 0)
2130
                        _flash_attn_backward(
2131
2132
2133
2134
2135
2136
2137
2138
2139
                            dout_,
                            q_,
                            kv_[0],
                            kv_[1],
                            out_,
                            softmax_lse,
                            dq_,
                            dkv_[0],
                            dkv_[1],
2140
2141
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2142
                            ctx.max_seqlen_q,
2143
                            ctx.max_seqlen_kv,
2144
2145
2146
2147
2148
                            ctx.dropout_p,
                            ctx.softmax_scale,
                            True,
                            rng_state=rng_states[cp_size - i - 1],
                            **fa_optional_backward_kwargs,
2149
                        )
2150
                elif i >= (cp_size - rank - 1):
2151
                    if ctx.use_fused_attention:
2152
2153
2154
                        if ctx.qkv_format == "bshd":
                            # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                            q_ = q.view(q.shape[0], -1, *q.shape[-2:])
2155
2156
                            # [b, 2, sk//2, 2, np, hn] -> [b, sk//2, 2, np, hn]
                            kv_ = kv[:, 0, ...].contiguous()
2157
2158
2159
2160
2161
2162
                            # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                            out_ = out.view(out.shape[0], -1, *out.shape[-2:])
                            dout_ = dout.view(dout.shape[0], -1, *dout.shape[-2:])
                        elif ctx.qkv_format == "sbhd":
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            q_ = q.view(-1, *q.shape[-3:])
2163
2164
                            # [2, sk//2, b, 2, np, hn] -> [sk//2, b, 2, np, hn]
                            kv_ = kv[0].contiguous()
2165
2166
2167
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            out_ = out.view(-1, *out.shape[-3:])
                            dout_ = dout.view(-1, *dout.shape[-3:])
2168
2169
2170
                        elif ctx.qkv_format == "thd":
                            q_, out_, dout_ = q, out, dout
                            # [2, t, np, hn] -> [2, t/2, np, hn]
2171
                            kv_ = tex.thd_read_half_tensor(kv, cu_seqlens_kv_padded, 0)
2172
                        aux_ctx_tensors = [softmax_lse, rng_states[cp_size - i - 1]]
2173
                        if attn_dbias is not None:
2174
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2175
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2176
                            ctx.max_seqlen_q,
2177
2178
2179
                            ctx.max_seqlen_kv // 2,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2180
                            q_,
2181
2182
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
2183
2184
2185
2186
2187
                            out_,
                            dout_,
                            TE_DType[q.dtype],
                            TE_DType[kv.dtype],
                            aux_ctx_tensors,
2188
                            tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
2189
2190
2191
2192
                            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
                            ),
2193
2194
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
2195
                            qkv_layout=qkv_layout,
2196
                            attn_mask_type="padding" if padding else "no_mask",
2197
                            attn_bias_type=ctx.attn_bias_type,
2198
2199
2200
2201
                        )
                    else:
                        # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                        q_ = q.view(-1, *q.shape[-2:])
2202
                        dq_ = torch.zeros_like(q_)
2203
2204
                        if ctx.qkv_format == "thd":
                            # [2, t, np, hn] -> [2, t/2, np, hn]
2205
                            kv_ = tex.thd_read_half_tensor(kv, cu_seqlens_kv_padded, 0)
2206
2207
2208
                        else:
                            # [2, b, 2, sk//2, np, hn]->[2, b, sk//2, np, hn]->[2, b*sk//2, np, hn]
                            kv_ = kv[:, :, 0, ...].contiguous().view(2, -1, *kv.shape[-2:])
2209
2210
2211
2212
2213
                        dkv_ = torch.empty_like(kv_)
                        # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                        out_ = out.view(-1, *out.shape[-2:])
                        dout_ = dout.view(-1, *dout.shape[-2:])
                        if _flash_attn_2_3_plus:
2214
                            fa_optional_backward_kwargs["window_size"] = (-1, -1)
2215
                        _flash_attn_backward(
2216
2217
2218
2219
2220
2221
2222
2223
2224
                            dout_,
                            q_,
                            kv_[0],
                            kv_[1],
                            out_,
                            softmax_lse,
                            dq_,
                            dkv_[0],
                            dkv_[1],
2225
2226
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2227
                            ctx.max_seqlen_q,
2228
                            ctx.max_seqlen_kv // 2,
2229
2230
2231
2232
2233
                            ctx.dropout_p,
                            ctx.softmax_scale,
                            False,
                            rng_state=rng_states[cp_size - i - 1],
                            **fa_optional_backward_kwargs,
2234
2235
2236
                        )
                else:
                    if ctx.use_fused_attention:
2237
2238
2239
                        if ctx.qkv_format == "bshd":
                            # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                            q_ = q[:, 1, ...].contiguous()
2240
2241
                            # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
                            kv_ = kv.view(kv.shape[0], -1, *kv.shape[-3:])
2242
2243
2244
2245
2246
2247
                            # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                            out_ = out[:, 1, ...].contiguous()
                            dout_ = dout[:, 1, ...].contiguous()
                        elif ctx.qkv_format == "sbhd":
                            # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                            q_ = q[1].contiguous()
2248
2249
                            # [2, sk//2, b, 2, np, hn] -> [sk, b, 2, np, hn]
                            kv_ = kv.view(-1, *kv.shape[-4:])
2250
2251
2252
                            # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                            out_ = out[1].contiguous()
                            dout_ = dout[1].contiguous()
2253
2254
                        elif ctx.qkv_format == "thd":
                            # [t, np, hn] -> [t/2, np, hn]
2255
2256
2257
                            q_ = tex.thd_read_half_tensor(q, cu_seqlens_q_padded, 1)
                            out_ = tex.thd_read_half_tensor(out, cu_seqlens_q_padded, 1)
                            dout_ = tex.thd_read_half_tensor(dout, cu_seqlens_q_padded, 1)
2258
                            kv_ = kv
2259
                        aux_ctx_tensors = [softmax_lse_, rng_states[cp_size - i - 1]]
2260
                        if attn_dbias is not None:
2261
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2262
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2263
                            ctx.max_seqlen_q // 2,
2264
2265
2266
                            ctx.max_seqlen_kv,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2267
                            q_,
2268
2269
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
2270
2271
2272
2273
2274
                            out_,
                            dout_,
                            TE_DType[q.dtype],
                            TE_DType[kv.dtype],
                            aux_ctx_tensors,
2275
                            tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
2276
2277
2278
2279
                            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,
2280
2281
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
2282
                            qkv_layout=qkv_layout,
2283
                            attn_mask_type="padding" if padding else "no_mask",
2284
                            attn_bias_type=ctx.attn_bias_type,
2285
2286
                        )
                    else:
2287
2288
                        if ctx.qkv_format == "thd":
                            # [t, np, hn] -> [t/2, np, hn]
2289
                            q_ = tex.thd_read_half_tensor(q, cu_seqlens_q_padded, 1)
2290
2291
2292
                        else:
                            # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn] -> [b*sq//2, np, hn]
                            q_ = q[:, 1, ...].contiguous().view(-1, *q.shape[-2:])
2293
                        dq_ = torch.zeros_like(q_)
2294
2295
2296
                        # [2, b, 2, sk//2, np, hn] -> [2, b*sk, np, hn]
                        kv_ = kv.view(2, -1, *kv.shape[-2:])
                        dkv_ = torch.empty_like(kv_)
2297
                        if ctx.qkv_format == "thd":
2298
2299
                            out_ = tex.thd_read_half_tensor(out, cu_seqlens_q_padded, 1)
                            dout_ = tex.thd_read_half_tensor(dout, cu_seqlens_q_padded, 1)
2300
2301
2302
2303
                        else:
                            # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn] -> [b*sq//2, np, hn]
                            out_ = out[:, 1, ...].contiguous().view(-1, *out.shape[-2:])
                            dout_ = dout[:, 1, ...].contiguous().view(-1, *dout.shape[-2:])
2304
                        if _flash_attn_2_3_plus:
2305
                            fa_optional_backward_kwargs["window_size"] = (-1, -1)
2306
                        _flash_attn_backward(
2307
2308
2309
2310
2311
2312
2313
2314
2315
                            dout_,
                            q_,
                            kv_[0],
                            kv_[1],
                            out_,
                            softmax_lse_,
                            dq_,
                            dkv_[0],
                            dkv_[1],
2316
2317
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2318
                            ctx.max_seqlen_q // 2,
2319
                            ctx.max_seqlen_kv,
2320
2321
2322
2323
2324
                            ctx.dropout_p,
                            ctx.softmax_scale,
                            False,
                            rng_state=rng_states[cp_size - i - 1],
                            **fa_optional_backward_kwargs,
2325
2326
2327
                        )
            else:
                if ctx.use_fused_attention:
2328
                    aux_ctx_tensors = [softmax_lse, rng_states[cp_size - i - 1]]
2329
                    if attn_dbias is not None:
2330
                        aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2331
                    dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2332
                        ctx.max_seqlen_q,
2333
2334
2335
                        ctx.max_seqlen_kv,
                        cu_seqlens_q_per_step[cp_size - i - 1],
                        cu_seqlens_kv_per_step[cp_size - i - 1],
2336
                        q,
2337
2338
                        kv[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv[0],
                        kv[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv[1],
2339
2340
2341
2342
2343
                        out,
                        dout,
                        TE_DType[q.dtype],
                        TE_DType[kv.dtype],
                        aux_ctx_tensors,
2344
                        tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
2345
2346
                        cu_seqlens_q_padded=cu_seqlens_q_padded,
                        cu_seqlens_kv_padded=cu_seqlens_kv_padded,
2347
2348
                        attn_scale=ctx.softmax_scale,
                        dropout=ctx.dropout_p,
2349
                        qkv_layout=qkv_layout,
2350
                        attn_mask_type=ctx.attn_mask_type,
2351
                        attn_bias_type=ctx.attn_bias_type,
2352
2353
2354
                    )
                else:
                    # [b, sq, np, hn] -> [b*sq, np, hn]
2355
                    q_ = q.view(-1, *q.shape[-2:])
2356
                    dq_ = torch.zeros_like(q_)
2357
                    # [2, b, sk, np, hn] -> [2, b*sk, np, hn]
2358
2359
                    kv_ = kv.view(2, -1, *kv.shape[-2:])
                    dkv_ = torch.empty_like(kv_)
2360
                    # [b, sq, np, hn] -> [b*sq, np, hn]
2361
2362
                    out_ = out.view(-1, *out.shape[-2:])
                    dout_ = dout.view(-1, *dout.shape[-2:])
2363
                    if _flash_attn_2_3_plus:
2364
                        fa_optional_backward_kwargs["window_size"] = (-1, -1)
2365
                    _flash_attn_backward(
2366
2367
2368
2369
2370
2371
2372
2373
2374
                        dout_,
                        q_,
                        kv_[0],
                        kv_[1],
                        out_,
                        softmax_lse,
                        dq_,
                        dkv_[0],
                        dkv_[1],
2375
2376
                        cu_seqlens_q_per_step[cp_size - i - 1],
                        cu_seqlens_kv_per_step[cp_size - i - 1],
2377
                        ctx.max_seqlen_q,
2378
                        ctx.max_seqlen_kv,
2379
2380
2381
                        ctx.dropout_p,
                        ctx.softmax_scale,
                        False,
2382
                        rng_state=rng_states[cp_size - i - 1],
2383
                        **fa_optional_backward_kwargs,
2384
2385
                    )

2386
            if i >= (cp_size - rank - 1) or not causal:
2387
2388
2389
2390
                # [b*sq, np, hn] -> [b, 2, sq//2, np, hn] if causal
                # [b*sq, np, hn] -> [b, sq, np, hn] if not causal
                dq_ = dq_.view(*dq.shape)
            else:
2391
2392
2393
2394
2395
2396
                if ctx.qkv_format == "bshd":
                    # [b*sq//2, np, hn] -> [b, sq//2, np, hn]
                    dq_ = dq_.view(dq.shape[0], *dq.shape[2:])
                elif ctx.qkv_format == "sbhd":
                    # [b*sq//2, np, hn] -> [sq//2, b, np, hn]
                    dq_ = dq_.view(-1, *dq.shape[-3:])
2397

2398
            if causal:
2399
                if i > (cp_size - rank - 1):
2400
                    dq.add_(dq_)
2401
2402
                elif i == (cp_size - rank - 1):
                    if rank == (cp_size - 1):
2403
2404
                        dq.copy_(dq_)
                    else:
2405
2406
2407
2408
2409
2410
                        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])
2411
                        elif ctx.qkv_format == "thd":
2412
                            tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "copy", "add")
2413
                elif i > 0:
2414
2415
2416
2417
                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].add_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].add_(dq_)
2418
                    elif ctx.qkv_format == "thd":
2419
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "none", "add")
2420
                else:
2421
2422
2423
2424
                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].copy_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].copy_(dq_)
2425
                    elif ctx.qkv_format == "thd":
2426
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "none", "copy")
2427
2428
2429
2430
2431
            else:
                if i == 0:
                    dq.copy_(dq_)
                else:
                    dq.add_(dq_)
2432

2433
            if attn_dbias is not None:
2434
                idx = (rank + i + 1) % cp_size
2435
                if i == (cp_size - 1) or not causal:
2436
                    # [b, np, sq, sk//cp] -> [b, np, sq, 2, sk//(2*cp)]
2437
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1] // 2)
2438
                    attn_dbias[..., idx, :].copy_(dbias_[..., 0, :])
2439
2440
                    attn_dbias[..., (2 * cp_size - idx - 1), :].copy_(dbias_[..., 1, :])
                elif i >= (cp_size - rank - 1):
2441
2442
2443
2444
                    # [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)]
2445
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1] // 2)
2446
                    attn_dbias_[..., 1, :, idx, :].copy_(dbias_[..., 0, :])
2447
                    attn_dbias_[..., 1, :, (2 * cp_size - idx - 1), :].copy_(dbias_[..., 1, :])
2448

2449
2450
2451
            # wait until dKV is received
            for req in send_recv_reqs:
                req.wait()
2452

2453
            dkv = p2p_comm_buffers[(i + 1) % 2][1]
2454
2455
            if ctx.use_fused_attention:
                dkv_ = torch.cat((dk_.unsqueeze(0), dv_.unsqueeze(0)), dim=0)
2456
2457
2458
2459
                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:])
2460
            if causal and i >= (cp_size - rank - 1) and i != (cp_size - 1):
2461
2462
2463
2464
2465
2466
                if ctx.qkv_format == "bshd":
                    # [2, b*sk//2, np, hn] -> [2, b, sk//2, np, hn]
                    dkv_ = dkv_.view(*dkv.shape[0:2], *dkv.shape[3:])
                elif ctx.qkv_format == "sbhd":
                    # [2, b*sk//2, np, hn] -> [2, sk//2, b, np, hn]
                    dkv_ = dkv_.view(dkv.shape[0], -1, *dkv.shape[-3:])
2467
2468
2469
2470
            else:
                # [2, b*sk, np, hn] -> [2, b, 2, sk//2, np, hn] if causal
                # [2, b*sk, np, hn] -> [2, b, sk, np, hn] if not causal
                dkv_ = dkv_.view(*dkv.shape)
2471

2472
            if causal:
2473
                if i == (cp_size - 1):
2474
                    if rank == 0:
2475
2476
2477
2478
2479
2480
                        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, ...])
2481
                        elif ctx.qkv_format == "thd":
2482
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "add", "copy")
2483
2484
                    else:
                        dkv.add_(dkv_)
2485
2486
                elif i >= (cp_size - rank - 1):
                    if i == 0 and rank == (cp_size - 1):
2487
2488
2489
2490
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].copy_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].copy_(dkv_)
2491
                        elif ctx.qkv_format == "thd":
2492
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "copy", "none")
2493
                    else:
2494
2495
2496
2497
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].add_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].add_(dkv_)
2498
                        elif ctx.qkv_format == "thd":
2499
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "add", "none")
2500
2501
2502
2503
2504
                elif i > 0:
                    dkv.add_(dkv_)
                else:
                    dkv.copy_(dkv_)
            else:
2505
2506
2507
2508
2509
                if i == 0:
                    dkv.copy_(dkv_)
                else:
                    dkv.add_(dkv_)

2510
        if causal:
2511
2512
            if ctx.qkv_format == "bshd":
                # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
2513
                dq = dq.view(dq.shape[0], -1, *dq.shape[-2:])
2514
                # [2, b, 2, sk//2, np, hn] -> [2, b, sk, np, hn]
2515
                dkv = dkv.view(*dkv.shape[0:2], -1, *dkv.shape[-2:])
2516
2517
            elif ctx.qkv_format == "sbhd":
                # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
2518
                dq = dq.view(-1, *dq.shape[-3:])
2519
                # [2, 2, sk//2, b, np, hn] -> [2, sk, b, np, hn]
2520
2521
2522
2523
2524
2525
2526
2527
2528
                dkv = dkv.view(dkv.shape[0], -1, *dkv.shape[-3:])

        if ctx.qkv_format == "thd":
            dkv_ = torch.empty(
                2, ctx.total_tokens_kv, *dkv.shape[-2:], dtype=dkv.dtype, device=dkv.device
            )
            dkv_[:, : cu_seqlens_kv_padded[-1]].copy_(dkv)
            dkv_[:, cu_seqlens_kv_padded[-1] :].fill_(0)
            dkv = dkv_
2529
2530
2531
2532
2533

        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)

2534
2535
2536
2537
2538
2539
2540
2541
2542
2543
2544
2545
2546
2547
2548
2549
2550
2551
2552
2553
2554
2555
2556
        return (
            None,
            dq,
            dkv[0],
            dkv[1],
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            attn_dbias,
            None,
            None,
        )
2557
2558


2559
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3007
@jit_fuser
def get_seq_chunk_ids_to_all_gathered_kv(
    local_chunk_id, cp_size, max_seqlen_q, max_seqlen_kv, window_size_left
):
    """Compute sequence chunk ids to the all-gathered KV."""
    seq_end_idx = (local_chunk_id + 1) * max_seqlen_kv
    seq_start_idx = max(0, seq_end_idx - max_seqlen_q - window_size_left)
    seqlen = seq_end_idx - seq_start_idx
    num_chunks = (seqlen + max_seqlen_kv - 1) // max_seqlen_kv
    chunk_ids = torch.arange(
        local_chunk_id - num_chunks + 1,
        local_chunk_id + 1,
        dtype=torch.int32,
        device="cuda",
    )
    chunk_ids_to_all_gathered_kv = torch.where(
        chunk_ids < cp_size, 2 * chunk_ids, 2 * (2 * cp_size - chunk_ids) - 1
    )
    return chunk_ids_to_all_gathered_kv


class AttnFuncWithCPAndKVAllGather(torch.autograd.Function):
    """
    Attention implementation with context parallelism.
    KV all-gather between CP ranks is exposed.
    """

    @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,
        cp_group,
        cp_stream,
        softmax_scale,
        qkv_format,
        attn_mask_type,
        attn_bias_type,
        attn_bias,
        deterministic,
        use_fused_attention,
        window_size,
    ):
        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)

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

        causal = "causal" in attn_mask_type
        padding = "padding" in attn_mask_type
        assert causal and not padding, f"{attn_mask_type} mask type is not supported!"
        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!"
        fa_optional_forward_kwargs = {}
        if _flash_attn_2_4_plus:
            fa_optional_forward_kwargs["alibi_slopes"] = None

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

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

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

        if causal:
            if qkv_format == "bshd":
                # [b, s, np, hn] -> [b, 2, s//2, np, hn]
                q = q.view(q.shape[0], 2, q.shape[1] // 2, *q.shape[2:])
                # [b, s, np, hn] -> [s, b, np, hn]
                k, v = [x.transpose(0, 1).contiguous() for x in [k, v]]
            elif qkv_format == "sbhd":
                # [s, b, np, hn] -> [2, s//2, b, np, hn]
                q = q.view(2, q.shape[0] // 2, *q.shape[1:])

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

        k_ag, _ = gather_along_first_dim(k, cp_group)
        v_ag, _ = gather_along_first_dim(v, cp_group)
        cp_stream.wait_stream(torch.cuda.current_stream())
        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:])

        local_seq_chunk_ids = [rank, 2 * cp_size - rank - 1]
        chunk_ids_to_kv_ag_per_step = [None, None]
        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]):
                    chunk_ids_to_kv_ag = get_seq_chunk_ids_to_all_gathered_kv(
                        local_seq_chunk_ids[i],
                        cp_size,
                        max_seqlen_q,
                        max_seqlen_kv,
                        (
                            max_seqlen_kv * cp_size * 2
                            if (window_size is None or window_size[0] == -1)
                            else window_size[0]
                        ),
                    )
                    chunk_ids_to_kv_ag_per_step[i] = chunk_ids_to_kv_ag
                    num_kv_chunks = chunk_ids_to_kv_ag.numel()
                    if qkv_format == "bshd":
                        # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                        q_ = q[:, i].contiguous()
                        # [num_kv_chunks, sq//2, b, np, hn] -> [b, num_kv_chunks*sq//2, np, hn]
                        k_ = (
                            torch.index_select(k_ag, dim=0, index=chunk_ids_to_kv_ag)
                            .movedim(2, 0)
                            .contiguous()
                            .view(k.shape[1], -1, *k.shape[-2:])
                        )
                        v_ = (
                            torch.index_select(v_ag, dim=0, index=chunk_ids_to_kv_ag)
                            .movedim(2, 0)
                            .contiguous()
                            .view(v.shape[1], -1, *v.shape[-2:])
                        )
                    elif qkv_format == "sbhd":
                        # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                        q_ = q[i].contiguous()
                        # [num_kv_chunks, sq//2, b, np, hn] -> [num_kv_chunks*sq//2, b, np, hn]
                        k_ = torch.index_select(k_ag, dim=0, index=chunk_ids_to_kv_ag).view(
                            -1, *k.shape[-3:]
                        )
                        v_ = torch.index_select(v_ag, dim=0, index=chunk_ids_to_kv_ag).view(
                            -1, *v.shape[-3:]
                        )
                    if use_fused_attention:
                        out_per_step[i], [softmax_lse_per_step[i], rng_states[i]] = fused_attn_fwd(
                            is_training,
                            max_seqlen_q,
                            max_seqlen_kv * num_kv_chunks,
                            cu_seqlens_q,
                            cu_seqlens_kv * num_kv_chunks,
                            q_,
                            k_,
                            v_,
                            TE_DType[q.dtype],
                            tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                            attn_scale=softmax_scale,
                            dropout=dropout_p,
                            qkv_layout=qkv_layout,
                            attn_mask_type=attn_mask_type,
                            attn_bias_type=attn_bias_type,
                            attn_bias=attn_bias,
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
                            cu_seqlens_kv_padded=cu_seqlens_kv_padded * num_kv_chunks,
                            window_size=window_size,
                        )
                    else:
                        q_, k_, v_ = [x.view(-1, *x.shape[-2:]) for x in [q_, k_, v_]]
                        _, _, _, _, out_per_step[i], softmax_lse_per_step[i], _, rng_states[i] = (
                            _flash_attn_forward(
                                q_,
                                k_,
                                v_,
                                cu_seqlens_q,
                                cu_seqlens_kv * num_kv_chunks,
                                max_seqlen_q,
                                max_seqlen_kv * num_kv_chunks,
                                dropout_p,
                                softmax_scale,
                                causal=True,
                                return_softmax=False,
                                window_size=window_size,
                                **fa_optional_forward_kwargs,
                            )
                        )

            if i > 0:
                with torch.cuda.stream(flash_attn_streams[i - 1]):
                    if qkv_format == "bshd":
                        out[:, i - 1].copy_(out_per_step[i - 1].view_as(out[:, i - 1]))
                    elif qkv_format == "sbhd":
                        out[i - 1].copy_(out_per_step[i - 1].view_as(out[i - 1]))

        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_kv,
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
            *chunk_ids_to_kv_ag_per_step,
            *out_per_step,
            *softmax_lse_per_step,
            *rng_states,
        )
        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.use_fused_attention = use_fused_attention
        ctx.window_size = window_size
        return out

    @staticmethod
    def backward(ctx, dout):
        cp_size = get_distributed_world_size(ctx.cp_group)
        rank = get_distributed_rank(ctx.cp_group)

        (q, k, v, cu_seqlens_q, cu_seqlens_kv, cu_seqlens_q_padded, cu_seqlens_kv_padded) = (
            ctx.saved_tensors[:7]
        )
        chunk_ids_to_kv_ag_per_step = ctx.saved_tensors[7:9]
        out_per_step = ctx.saved_tensors[9:11]
        softmax_lse_per_step = ctx.saved_tensors[11:13]
        rng_states = ctx.saved_tensors[13:15]

        qkv_layout = ctx.qkv_format + "_" + ctx.qkv_format + "_" + ctx.qkv_format

        dout = dout.view_as(q)
        dq = torch.empty_like(q)
        dk = torch.zeros(
            (2 * cp_size, k.shape[0] // 2, *k.shape[1:]), dtype=k.dtype, device=k.device
        )
        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()

        k_ag, _ = gather_along_first_dim(k, ctx.cp_group)
        v_ag, _ = gather_along_first_dim(v, ctx.cp_group)
        ctx.cp_stream.wait_stream(torch.cuda.current_stream())
        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:])

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

        fa_optional_backward_kwargs = {}
        if _flash_attn_2_4_plus:
            fa_optional_backward_kwargs["alibi_slopes"] = None
        if _flash_attn_2_4_1_plus:
            fa_optional_backward_kwargs["deterministic"] = ctx.deterministic

        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]):
                    chunk_ids_to_kv_ag = chunk_ids_to_kv_ag_per_step[i]
                    num_kv_chunks = chunk_ids_to_kv_ag.numel()
                    out_ = out_per_step[i]
                    if ctx.qkv_format == "bshd":
                        # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                        q_ = q[:, i].contiguous()
                        # [num_kv_chunks, sq//2, b, np, hn] -> [b, num_kv_chunks*sq//2, np, hn]
                        k_ = (
                            torch.index_select(k_ag, dim=0, index=chunk_ids_to_kv_ag)
                            .movedim(2, 0)
                            .contiguous()
                            .view(k.shape[1], -1, *k.shape[-2:])
                        )
                        v_ = (
                            torch.index_select(v_ag, dim=0, index=chunk_ids_to_kv_ag)
                            .movedim(2, 0)
                            .contiguous()
                            .view(v.shape[1], -1, *v.shape[-2:])
                        )
                        dout_ = dout[:, i].contiguous().view_as(out_)
                    elif ctx.qkv_format == "sbhd":
                        # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                        q_ = q[i].contiguous()
                        # [num_kv_chunks, sq//2, b, np, hn] -> [num_kv_chunks*sq//2, b, np, hn]
                        k_ = torch.index_select(k_ag, dim=0, index=chunk_ids_to_kv_ag).view(
                            -1, *k.shape[-3:]
                        )
                        v_ = torch.index_select(v_ag, dim=0, index=chunk_ids_to_kv_ag).view(
                            -1, *v.shape[-3:]
                        )
                        dout_ = dout[i].contiguous().view_as(out_)
                    if ctx.use_fused_attention:
                        dq_per_step[i], dk_per_step[i], dv_per_step[i] = [
                            torch.empty_like(x) for x in [q_, k_, v_]
                        ]
                        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,
                            ctx.max_seqlen_kv * num_kv_chunks,
                            cu_seqlens_q,
                            cu_seqlens_kv * num_kv_chunks,
                            q_,
                            k_,
                            v_,
                            out_,
                            dout_,
                            TE_DType[q.dtype],
                            TE_DType[k.dtype],
                            aux_ctx_tensors,
                            tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
                            cu_seqlens_kv_padded=cu_seqlens_kv_padded * num_kv_chunks,
                            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,
                        )
                    else:
                        q_, k_, v_ = [x.view(-1, *x.shape[-2:]) for x in [q_, k_, v_]]
                        dq_per_step[i], dk_per_step[i], dv_per_step[i] = [
                            torch.empty_like(x) for x in [q_, k_, v_]
                        ]
                        _flash_attn_backward(
                            dout_,
                            q_,
                            k_,
                            v_,
                            out_,
                            softmax_lse_per_step[i],
                            dq_per_step[i],
                            dk_per_step[i],
                            dv_per_step[i],
                            cu_seqlens_q,
                            cu_seqlens_kv * num_kv_chunks,
                            ctx.max_seqlen_q,
                            ctx.max_seqlen_kv * num_kv_chunks,
                            ctx.dropout_p,
                            ctx.softmax_scale,
                            True,
                            window_size=ctx.window_size,
                            rng_state=rng_states[i],
                            **fa_optional_backward_kwargs,
                        )

            if i > 0:
                with torch.cuda.stream(flash_attn_streams[i - 1]):
                    chunk_ids_to_kv_ag = chunk_ids_to_kv_ag_per_step[i - 1]
                    num_kv_chunks = chunk_ids_to_kv_ag.numel()
                    if ctx.qkv_format == "bshd":
                        dq[:, i - 1].copy_(dq_per_step[i - 1].view_as(dq[:, i - 1]))
                        dk_per_step[i - 1] = (
                            dk_per_step[i - 1]
                            .view(k.shape[1], num_kv_chunks, -1, *k.shape[-2:])
                            .movedim(0, 2)
                            .contiguous()
                        )
                        dv_per_step[i - 1] = (
                            dv_per_step[i - 1]
                            .view(v.shape[1], num_kv_chunks, -1, *v.shape[-2:])
                            .movedim(0, 2)
                            .contiguous()
                        )
                    elif ctx.qkv_format == "sbhd":
                        dq[i - 1].copy_(dq_per_step[i - 1].view_as(dq[i - 1]))
                        dk_per_step[i - 1] = dk_per_step[i - 1].view(
                            num_kv_chunks, -1, *k.shape[-3:]
                        )
                        dv_per_step[i - 1] = dv_per_step[i - 1].view(
                            num_kv_chunks, -1, *v.shape[-3:]
                        )

                    # wait until dkv update of last step is done
                    if i > 1:
                        flash_attn_streams[i - 1].wait_event(dkv_update_done)
                    dk.index_add_(0, chunk_ids_to_kv_ag, dk_per_step[i - 1])
                    dv.index_add_(0, chunk_ids_to_kv_ag, dv_per_step[i - 1])
                    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)

        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)

        if ctx.qkv_format == "bshd":
            dq = dq.view(dq.shape[0], -1, *dq.shape[-2:])
            dk = dk.transpose(0, 1).contiguous()
            dv = dv.transpose(0, 1).contiguous()
        elif ctx.qkv_format == "sbhd":
            dq = dq.view(-1, *dq.shape[-3:])

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


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def attn_forward_func_with_cp(
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    is_training,
    q,
    k,
    v,
    cu_seqlens_q,
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    cu_seqlens_kv,
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    max_seqlen_q,
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    max_seqlen_kv,
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    cu_seqlens_q_padded,
    cu_seqlens_kv_padded,
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    dropout_p,
    cp_group,
    cp_global_ranks,
    cp_stream,
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    cp_comm_type,
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    softmax_scale=None,
    qkv_format="bshd",
    attn_mask_type="causal",
    attn_bias_type="no_bias",
    attn_bias=None,
    deterministic=False,
    use_fused_attention=False,
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    window_size=None,
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) -> torch.Tensor:
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    """
    Attention implementation with context parallelism.
    """

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    assert qkv_format in [
        "bshd",
        "sbhd",
        "thd",
    ], f"QKV format of {qkv_format} is not supported with context parallelism!"
    assert (
        qkv_format != "sbhd" or use_fused_attention
    ), "FlashAttention does not support sbhd format!"
    assert (
        qkv_format != "thd"
        or not use_fused_attention
        or attn_mask_type in ["padding", "padding_causal"]
    ), (
        f"Context parallelism is not supported for {attn_mask_type} mask type and "
        f"{qkv_format} format with {'FusedAttention' if use_fused_attention else 'FlashAttention'}!"
    )
    assert attn_bias is None or (use_fused_attention and "padding" not in attn_mask_type), (
        """Attention bias is only supported with FusedAttention and "causal" """
        """or "no_mask" mask types!"""
    )
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    assert (
        cu_seqlens_q_padded is not None and cu_seqlens_kv_padded is not None
    ), "cu_seqlens_q_padded and cu_seqlens_kv_padded cannot be None with context parallelism!"
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    sliding_window_attn = (
        window_size is not None and window_size != (-1, 0) and window_size != (-1, -1)
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    )
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    if sliding_window_attn or cp_comm_type == "all_gather":
        out = AttnFuncWithCPAndKVAllGather.apply(
            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,
            cp_group,
            cp_stream,
            softmax_scale,
            qkv_format,
            attn_mask_type,
            attn_bias_type,
            attn_bias,
            deterministic,
            use_fused_attention,
            window_size,
        )
    elif cp_comm_type == "p2p":
        out = AttnFuncWithCPAndKVP2P.apply(
            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,
            cp_group,
            cp_global_ranks,
            cp_stream,
            softmax_scale,
            qkv_format,
            attn_mask_type,
            attn_bias_type,
            attn_bias,
            deterministic,
            use_fused_attention,
        )
    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,
    ):
        """
        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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        inv_freq = 1.0 / (
            10000
            ** (
                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,
    ) -> torch.Tensor:
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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":
            output = tex.fused_rope_thd_forward(t, cu_seqlens, freqs)
        else:
            raise ValueError(f"Unsupported tensor_format: {tensor_format}.")
        ctx.save_for_backward(freqs, cu_seqlens)
        ctx.tensor_format = tensor_format

        return output

    @staticmethod
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    def backward(ctx, grad_output: torch.Tensor) -> Tuple[Union[torch.Tensor, None], ...]:
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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":
            grad_input = tex.fused_rope_thd_backward(grad_output, cu_seqlens, freqs)
        else:
            raise ValueError(f"Unsupported tensor_format: {ctx.tensor_format}.")

        return grad_input, 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,
) -> 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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    """
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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'."
        return FusedRoPEFunc.apply(t, freqs, tensor_format, cu_seqlens)

    assert tensor_format in ("sbhd", "bshd"), (
        "Only formats `sbhd` or `bshd` are supported for input tensor `t` "
        f"when fused is False, got {tensor_format}."
    )

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

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

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


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

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

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

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

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

    def __init__(
        self,
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        softmax_scale: float,
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        attention_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_dropout_ctx = attention_dropout_ctx
        self.layer_number = layer_number

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

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        # An FP16 training trick required for certain GPT-like models.
        self.apply_qk_layer_scaling = (
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            bool(int(os.getenv("NVTE_APPLY_QK_LAYER_SCALING", "0"))) and layer_number is not None
        )
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    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
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        qkv_layout: str = "sbh3d",
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        cu_seqlens_q: Optional[torch.Tensor] = None,  # pylint: disable=unused-argument
        cu_seqlens_kv: Optional[torch.Tensor] = None,  # pylint: disable=unused-argument
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        attn_mask_type: str = "causal",
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        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
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        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
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        alibi_slopes: Optional[torch.Tensor] = None,
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    ) -> torch.Tensor:
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        """Unfused attention fprop"""
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        assert (
            qkv_layout in QKVLayouts
        ), f"UnfusedDotProductAttention does not support qkv_layout = {qkv_layout}!"
        qkv_format = "".join([i for i in qkv_layout.split("_")[0] if i.isalpha()])
        if qkv_format == "bshd":
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            # convert to sbhd and use sbhd implementation for now
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            query_layer, key_layer, value_layer = [
                x.transpose(0, 1) for x in [query_layer, key_layer, value_layer]
            ]
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        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
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        # [b, np, sq, sk]
        output_size = (
            query_layer.size(1),
            query_layer.size(2),
            query_layer.size(0),
            key_layer.size(0),
        )

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        if key_layer.shape[2] != query_layer.shape[2]:
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            assert (
                query_layer.shape[2] % key_layer.shape[2] == 0
            ), "The number of attention heads must be divisible by the number of GQA groups!"
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            key_layer = key_layer.repeat_interleave(
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                int(query_layer.shape[2] / key_layer.shape[2]), dim=2
            )
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            value_layer = value_layer.repeat_interleave(
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                int(query_layer.shape[2] / value_layer.shape[2]), dim=2
            )
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        # [sq, b, np, hn] -> [sq, b * np, hn]
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        query_layer = query_layer.reshape(output_size[2], output_size[0] * output_size[1], -1)
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        # [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]
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        # WAR to set dtype to FP32 as ONNX lacks BF16 support for ConstantOfShape operator
        is_bf16 = query_layer.dtype == torch.bfloat16
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        matmul_result = torch.empty(
            output_size[0] * output_size[1],
            output_size[2],
            output_size[3],
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            dtype=torch.float32 if is_in_onnx_export_mode() and is_bf16 else query_layer.dtype,
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            device=torch.cuda.current_device(),
        )

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        if is_in_onnx_export_mode() and is_bf16:
            matmul_result = matmul_result.bfloat16()

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        scale = self.softmax_scale
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        if apply_qk_layer_scaling:
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            scale /= self.layer_number
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        # Raw attention scores. [b * np, sq, sk]
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        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,
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                alpha=scale,
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            )

        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]
            )
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            matmul_result = (
                matmul_result.view(output_size[0], output_size[1], output_size[2], output_size[3])
                + core_attention_bias
            ).view(-1, output_size[2], output_size[3])
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            matmul_result *= scale
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        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":
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                _, core_attention_bias = get_alibi(
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                    output_size[1],
                    output_size[2],
                    output_size[3],
                    alibi_slopes=alibi_slopes,
                    bottom_right_alignment=attn_mask_type not in ["causal", "padding_causal"],
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                )
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            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,
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                alpha=scale,
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            )
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            matmul_result = (
                (
                    matmul_result.view(
                        output_size[0], output_size[1], output_size[2], output_size[3]
                    )
                    + core_attention_bias
                )
                .view(-1, output_size[2], output_size[3])
                .to(dtype=query_layer.dtype)
            )
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        # change view to [b, np, sq, sk]
        attention_scores = matmul_result.view(*output_size)

        # attention scores and attention mask [b, np, sq, sk]
        softmax_scale = self.layer_number if apply_qk_layer_scaling else None
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        attention_probs = self.scale_mask_softmax(
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            attention_scores, attention_mask, attn_mask_type, softmax_scale
        )
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3598
3599
3600
3601

        # 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]
3602
        value_layer = value_layer.reshape(value_layer.size(0), output_size[0] * output_size[1], -1)
3603
3604

        # change view [b * np, sq, sk]
3605
        attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
3606
3607
3608
3609
3610
3611
3612

        # 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)

3613
        if qkv_format == "sbhd":
3614
3615
            # [b, np, sq, hn] --> [sq, b, np, hn]
            context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
3616

3617
3618
3619
            # [sq, b, np, hn] --> [sq, b, hp]
            context_layer = context_layer.view(seqlen, batch_size, -1)

3620
        if qkv_format == "bshd":
3621
3622
3623
3624
3625
            # [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)
3626
3627
3628
3629
3630
3631

        return context_layer


class _PrepareQKVForFA(torch.autograd.Function):
    """This class converts QKV from interleaved (s, b, ...) layout
3632
    to separate contiguous q, k, v tensors in (b, s, ...) layout."""
3633
3634

    @staticmethod
3635
3636
3637
3638
    def forward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
3639
        value_layer: torch.Tensor,
3640
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
3641
3642
3643
3644
3645
3646
3647
3648
3649
3650
3651
        # 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
3652
3653
3654
3655
    def backward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        dq: torch.Tensor,
        dk: torch.Tensor,
3656
        dv: torch.Tensor,
3657
3658
3659
3660
3661
    ) -> Tuple[Union[torch.Tensor, None], ...]:
        dqkv = tex.fa_prepare_bwd(dq, dk, dv)
        dq, dk, dv = split_tensor_along_dim(dqkv, -1, 3)
        return dq, dk, dv

3662

3663
def get_qkv_layout(
3664
3665
3666
3667
3668
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    qkv_format: str = "sbhd",
) -> str:
3669
    """Get qkv layout.
3670

3671
3672
3673
3674
3675
3676
3677
3678
3679
3680
3681
    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,
3682
        `d` head size, and `t` the total number of tokens in a batch, i.e.
3683
3684
3685
3686
3687
3688
3689
3690
3691
3692
3693
3694
3695
3696
3697
3698
        `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`}
    """
3699

3700
3701
    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!"
3702

3703
3704
3705
3706
3707
3708
3709
3710
3711
    def run_iteratively(q, k, v):
        data_ptr = q.untyped_storage().data_ptr()
        check_ptrs_qkv = all(x.untyped_storage().data_ptr() == data_ptr for x in [q, k, v])
        data_ptr = k.untyped_storage().data_ptr()
        check_ptrs_kv = all(x.untyped_storage().data_ptr() == data_ptr for x in [k, v])

        stride = q.stride()
        check_strides_qkv = all(stride == x.stride() for x in [q, k, v])
        stride = k.stride()
3712
3713
3714
        check_strides_kv = torch.equal(
            torch.Tensor(stride[:-1]) / k.shape[-1], torch.Tensor(v.stride()[:-1]) / v.shape[-1]
        )
3715
3716
3717
3718

        shape = q.shape
        check_shapes_qkv = all(shape == x.shape for x in [q, k, v])
        shape = k.shape
3719
        check_shapes_kv = shape[:-1] == v.shape[:-1]
3720
3721

        last_dim_size = q.shape[-1]
3722
3723
3724
        check_last_dim_offsets_qkv = all(
            i * last_dim_size == x.storage_offset() for i, x in enumerate([q, k, v])
        )
3725
        last_dim_size = k.shape[-1]
3726
3727
3728
        check_last_dim_offsets_kv = all(
            i * last_dim_size == x.storage_offset() for i, x in enumerate([k, v])
        )
3729
3730

        last_two_dims_size = q.shape[-1] * q.shape[-2]
3731
3732
3733
        check_last_two_dims_offsets_qkv = all(
            i * last_two_dims_size == x.storage_offset() for i, x in enumerate([q, k, v])
        )
3734
        last_two_dims_size = k.shape[-1] * k.shape[-2]
3735
3736
3737
        check_last_two_dims_offsets_kv = all(
            i * last_two_dims_size == x.storage_offset() for i, x in enumerate([k, v])
        )
3738

3739
3740
3741
3742
        if (
            check_ptrs_qkv
            and check_strides_qkv
            and check_shapes_qkv
3743
            and check_last_two_dims_offsets_qkv
3744
3745
            and not check_last_dim_offsets_qkv
        ):
3746
            # sb3hd, bs3hd, t3hd
3747
3748
3749
3750
            qkv_layout = qkv_format[:-2] + "3" + qkv_format[-2:]
        elif (
            check_ptrs_qkv and check_strides_qkv and check_shapes_qkv and check_last_dim_offsets_qkv
        ):
3751
            # sbh3d, bsh3d, th3d
3752
3753
3754
3755
3756
            qkv_layout = qkv_format[:-1] + "3" + qkv_format[-1:]
        elif (
            check_ptrs_kv
            and check_strides_kv
            and check_shapes_kv
3757
            and check_last_two_dims_offsets_kv
3758
3759
            and not check_last_dim_offsets_kv
        ):
3760
            # sbhd_sb2hd, bshd_bs2hd, thd_t2hd
3761
3762
            qkv_layout = qkv_format + "_" + qkv_format[:-2] + "2" + qkv_format[-2:]
        elif check_ptrs_kv and check_strides_kv and check_shapes_kv and check_last_dim_offsets_kv:
3763
            # sbhd_sbh2d, bshd_bsh2d, thd_th2d
3764
            qkv_layout = qkv_format + "_" + qkv_format[:-1] + "2" + qkv_format[-1:]
3765
3766
        elif check_strides_kv and check_shapes_kv:
            # sbhd_sbhd_sbhd, bshd_bshd_bshd, thd_thd_thd
3767
            qkv_layout = "_".join(list([qkv_format]) * 3)
3768
        else:
3769
            qkv_layout = "not_supported"
3770
3771
3772
3773

        return qkv_layout

    qkv_layout = run_iteratively(q, k, v)
3774
    if qkv_layout == "not_supported":
3775
3776
3777
        # 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)
3778
    if qkv_layout == "not_supported":
3779
3780
        raise Exception("The provided qkv memory layout is not supported!")

3781
    return qkv_layout, q, k, v
3782

3783

3784
def check_set_window_size(
3785
3786
3787
    attn_mask_type: str,
    window_size: Tuple[int, int] = None,
):
3788
3789
3790
3791
3792
3793
3794
3795
    """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)
3796
    """
3797
    orig_window_size = window_size
3798
    if "causal" in attn_mask_type:
3799
        if orig_window_size is None:
3800
            window_size = (-1, 0)
3801
3802
3803
        elif orig_window_size == (-1, -1) or (
            orig_window_size[0] >= 0 and orig_window_size[1] != 0
        ):
3804
3805
3806
3807
            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
            )
3808
        elif orig_window_size != (-1, 0) and (orig_window_size[0] < 0 or orig_window_size[1] != 0):
3809
3810
3811
3812
            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"]:
3813
3814
3815
        if orig_window_size is None:
            window_size = (-1, -1)
        elif orig_window_size == (-1, 0):
3816
            window_size = (-1, -1)
3817
3818
3819
            warnings.warn(
                "window_size should be (-1, -1) or (>=0, >=0) for attn_mask_type=" + attn_mask_type
            )
3820
        elif orig_window_size != (-1, -1) and (orig_window_size[0] < 0 or orig_window_size[1] < 0):
3821
3822
3823
3824
3825
            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
3826
    return window_size
3827

3828

3829
class FlashAttention(torch.nn.Module):
3830
    """Dot product attention, using HazyResearch flash-attn package:
3831
    https://github.com/Dao-AILab/flash-attention
3832
3833
3834
3835
    """

    def __init__(
        self,
3836
        softmax_scale: float,
3837
3838
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
3839
3840
        attention_type: str = "self",
        layer_number: Optional[int] = None,
3841
        deterministic: bool = False,
3842
3843
3844
3845
3846
3847
    ) -> None:
        super().__init__()

        assert (
            _flash_attn_version >= _flash_attn_version_required
        ), f"FlashAttention minimum version {_flash_attn_version_required} is required."
3848
3849
3850
        assert (
            _flash_attn_version <= _flash_attn_max_version
        ), f"FlashAttention maximum version {_flash_attn_max_version} is supported."
3851

3852
        self.softmax_scale = softmax_scale
3853
3854
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_dropout = attention_dropout
3855
3856
        self.attention_type = attention_type
        self.layer_number = 1 if layer_number is None else layer_number
3857
        self.deterministic = deterministic
3858
3859
3860
3861
3862
3863

    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
3864
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
3865
3866
3867
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
3868
3869
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
3870
        attn_mask_type: str = "causal",
3871
        window_size: Optional[Tuple[int, int]] = None,
3872
        alibi_slopes: Optional[torch.Tensor] = None,
3873
        cp_group: Optional[dist_group_type] = None,
3874
        cp_global_ranks: List[int] = None,
3875
        cp_stream: torch.cuda.Stream = None,
3876
        cp_comm_type: str = "p2p",
3877
3878
3879
3880
    ) -> torch.Tensor:
        """flash-attn fprop"""

        assert (
3881
3882
3883
            query_layer.dtype in [torch.float16, torch.bfloat16]
            and key_layer.dtype in [torch.float16, torch.bfloat16]
            and value_layer.dtype in [torch.float16, torch.bfloat16]
3884
        ), "FlashAttention currently only supports FP16 and BF16."
3885
3886
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
3887
        ), "FlashAttention currently only supports CUDA tensors."
3888
3889
        assert (
            qkv_layout in QKVLayouts
3890
        ), f"FlashAttention does not support qkv_layout = {qkv_layout}!"
3891

3892
3893
        cp_size = 1 if cp_group is None else get_distributed_world_size(cp_group)
        context_parallel = cp_size > 1
3894

3895
        qkv_format = "".join([i for i in qkv_layout.split("_")[0] if i.isalpha()])
3896

3897
        if qkv_format == "sbhd":
3898
            # For now just 128, will make it more general in the future
3899
3900
3901
3902
3903
3904
3905
3906
            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
                )
3907
            else:
3908
3909
3910
3911
3912
3913
3914
                query_layer, key_layer, value_layer = [
                    x.transpose(0, 1).contiguous() for x in (query_layer, key_layer, value_layer)
                ]
        elif qkv_format in ["bshd", "thd"]:
            query_layer, key_layer, value_layer = [
                x.contiguous() for x in (query_layer, key_layer, value_layer)
            ]
3915

3916
        batch_size = query_layer.shape[0]
3917

3918
        if qkv_format in ["sbhd", "bshd"]:
3919
            max_seqlen_q, max_seqlen_kv = query_layer.shape[1], key_layer.shape[1]
3920
3921
            max_seqlen_q *= cp_size
            max_seqlen_kv *= cp_size
3922
3923
3924
3925
3926
3927
3928
            if not context_parallel:
                # [b * s, h, d]
                query_layer, key_layer, value_layer = [
                    x.view(x.shape[0] * x.shape[1], *x.shape[2:])
                    for x in [query_layer, key_layer, value_layer]
                ]

3929
            if "padding" in attn_mask_type:
3930
                assert not context_parallel, "Padding mask not supported with context parallelism!"
3931
3932
3933
3934
3935

                if self.attention_type == "self":
                    assert (
                        max_seqlen_q == max_seqlen_kv
                    ), "Maximum sequence length for Q and KV should be the same."
3936
                    if cu_seqlens_q is None:
3937
3938
3939
                        assert (
                            attention_mask is not None
                        ), "Please provide attention_mask for padding!"
3940
3941
3942
3943
3944
3945
                        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
3946
3947
                    )
                else:
3948
                    if cu_seqlens_q is None or cu_seqlens_kv is None:
3949
3950
3951
3952
3953
                        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])
3954
3955
3956
3957
                    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)
3958
                    key_layer, value_layer = PackTensors.apply(indices_kv, key_layer, value_layer)
3959
            else:
3960
3961
3962
3963
3964
3965
3966
3967
3968
3969
3970
3971
3972
                # 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,
                    )
3973
3974
3975
3976
        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!"
3977
3978
3979
3980
3981
3982
            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()
3983

3984
        if context_parallel:
3985
3986
3987
            assert (
                alibi_slopes is None
            ), "Alibi slope bias addition is not supported with context parallelism."
3988
            with self.attention_dropout_ctx():
3989
                output = attn_forward_func_with_cp(
3990
3991
3992
3993
3994
3995
3996
3997
                    self.training,
                    query_layer,
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
3998
3999
                    cu_seqlens_q,
                    cu_seqlens_kv,
4000
                    self.attention_dropout if self.training else 0.0,
4001
4002
4003
                    cp_group,
                    cp_global_ranks,
                    cp_stream,
4004
                    cp_comm_type,
4005
                    softmax_scale=self.softmax_scale,
4006
                    qkv_format="bshd" if qkv_format == "sbhd" else qkv_format,
4007
                    attn_mask_type=attn_mask_type,
4008
                    deterministic=self.deterministic,
4009
                    window_size=window_size,
4010
4011
                )
        else:
4012
4013

            from .cpu_offload import CPUOffloadEnabled
4014

4015
4016
4017
4018
4019
4020
            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

4021
            with self.attention_dropout_ctx():
4022
                fa_optional_forward_kwargs = {}
4023
4024
                if _flash_attn_2_3_plus:
                    fa_optional_forward_kwargs["window_size"] = window_size
4025
4026
4027
4028
                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
4029
4030
                if _flash_attn_2_5_7_plus:
                    fa_optional_forward_kwargs["block_table"] = None
4031
                output = flash_attn_forward_func(
4032
4033
4034
4035
4036
4037
4038
                    query_layer,
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
4039
                    self.attention_dropout if self.training else 0.0,
4040
4041
                    softmax_scale=self.softmax_scale,
                    causal="causal" in attn_mask_type,
4042
                    **fa_optional_forward_kwargs,
4043
                )
4044

4045
        if qkv_format in ["sbhd", "bshd"] and "padding" in attn_mask_type:
4046
            output = UnpackTensor.apply(indices_q, batch_size * max_seqlen_q, output)
4047

4048
        if qkv_format == "sbhd":
4049
            # (bs)hd -> bs(hd) -> sb(hd)
4050
4051
4052
            output = (
                output.view(batch_size, max_seqlen_q // cp_size, -1).transpose(0, 1).contiguous()
            )
4053
        elif qkv_format == "bshd":
4054
            # (bs)hd -> bs(hd)
4055
            output = output.view(batch_size, max_seqlen_q // cp_size, -1).contiguous()
4056
        elif qkv_format == "thd":
4057
4058
            # thd -> t(hd)
            output = output.view(output.shape[0], -1).contiguous()
4059
4060

        return output
4061

4062

4063
def _combine_tensors(
4064
4065
4066
    tensors: List[torch.Tensor],
    dim: int,
) -> torch.Tensor:
4067
4068
4069
4070
4071
4072
    """Combine tensors along a particular dimension"""

    num_tensors = len(tensors)
    new_shape = list(tensors[0].shape)
    new_shape.insert(dim, num_tensors)
    new_stride = list(tensors[0].stride())
4073
    new_stride.insert(dim, int(new_stride[dim - 1] / num_tensors))
4074
    if isinstance(tensors[0], Float8Tensor):
4075
        combined_tensor = torch.Tensor().to(device=tensors[0].device, dtype=tensors[0]._data.dtype)
4076
4077
4078
        combined_tensor.set_(
            tensors[0]._data.untyped_storage(),
            tensors[0]._data.storage_offset(),
4079
4080
4081
4082
            new_shape,
            new_stride,
        )
        combined_tensor = Float8Tensor.make_like(tensors[0], data=combined_tensor)
4083
    else:
4084
        combined_tensor = torch.Tensor().to(device=tensors[0].device, dtype=tensors[0].dtype)
4085
        combined_tensor.set_(
4086
4087
            tensors[0].untyped_storage(), tensors[0].storage_offset(), new_shape, new_stride
        )
4088
4089

    return combined_tensor
4090

4091

4092
4093
4094
4095
class FusedAttnFunc_qkvpacked(torch.autograd.Function):
    """Function for FusedAttention with packed QKV input"""

    @staticmethod
4096
4097
4098
4099
4100
    def forward(
        ctx,
        is_training,
        max_seqlen,
        cu_seqlens,
4101
        cu_seqlens_padded,
4102
4103
4104
4105
4106
4107
4108
4109
4110
        qkv,
        qkv_dtype,
        attn_bias,
        attn_scale,
        dropout_p,
        fast_zero_fill,
        qkv_layout,
        attn_bias_type,
        attn_mask_type,
4111
        window_size,
4112
4113
4114
4115
4116
        rng_gen,
        fused_attention_backend,
        use_FAv2_bwd,
        fp8,
        fp8_meta,
4117
        deterministic,
4118
    ):
4119
4120
        if fp8:
            if fp8_meta["recipe"].fp8_mha:
4121
                assert isinstance(qkv, Float8Tensor), "qkv must be Float8Tensors for FP8 MHA."
4122
4123
4124
4125
                fp8_meta["scaling_fwd"].scale_inv[META_QKV] = qkv._scale_inv
            fused_attention_backend = FusedAttnBackend["FP8"]
            fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
            # 1: qkv packed, 2: kv packed, 3: qkv separate
4126
4127
4128
4129
4130
            qkv_group = len(qkv_layout.split("_"))
            assert qkv_group == 1, (
                "qkv layout should conform to 3hd or h3d, e.g. sb3hd,                 but found"
                f" {qkv_layout}."
            )
4131
4132
4133
4134
            if fp8_meta["recipe"].fp8_mha:
                qkv_fp8 = qkv._data
            else:
                qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
4135
4136
4137
                qkv_fp8 = cast_to_fp8(
                    qkv_c, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                ).view(qkv.shape)
4138
            out_fp8, aux_ctx_tensors = fused_attn_fwd_qkvpacked(
4139
4140
4141
4142
4143
4144
4145
                is_training,
                max_seqlen,
                cu_seqlens,
                qkv_fp8,
                fp8_dtype_forward,
                fused_attention_backend,
                attn_bias,
4146
                cu_seqlens_padded,
4147
4148
4149
4150
4151
4152
                fp8_meta["scaling_fwd"].scale_inv[META_QKV],
                fp8_meta["scaling_fwd"].scale_inv[META_S],
                fp8_meta["scaling_fwd"].scale[META_S],
                fp8_meta["scaling_fwd"].scale[META_O],
                fp8_meta["scaling_fwd"].amax_history[0][META_S],
                fp8_meta["scaling_fwd"].amax_history[0][META_O],
4153
4154
4155
4156
4157
4158
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
4159
                window_size,
4160
4161
                rng_gen,
            )
4162
            if fp8_meta["recipe"].fp8_mha:
4163
4164
                out_ret = Float8Tensor(
                    data=out_fp8,
4165
4166
4167
4168
4169
4170
4171
4172
4173
                    fp8_meta=fp8_meta,
                    fp8_meta_forward=True,
                    fp8_meta_index=META_O,
                    fp8_dtype=fp8_dtype_forward,
                    dtype=qkv.dtype,
                )
            else:
                out_ret = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
4174
4175
4176
4177
4178
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    qkv_dtype,
                ).view(out_fp8.shape)
4179
4180
4181
            out_save = out_ret
            if fp8_meta["recipe"].fp8_mha and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
4182
4183
                qkv = cast_from_fp8(
                    qkv_c._data,
4184
                    fp8_meta["scaling_fwd"],
4185
4186
4187
4188
                    META_QKV,
                    fp8_dtype_forward,
                    TE_DType[qkv.dtype],
                ).view(qkv.shape)
4189
4190
                out_save = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
4191
4192
4193
4194
4195
4196
4197
4198
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    qkv_dtype,
                ).view(out_fp8.shape)
            fp8_tensors = (
                qkv_fp8,
                out_fp8,
4199
                fp8_meta["scaling_fwd"].scale.clone(),
4200
4201
                fp8_meta["scaling_fwd"].scale_inv.clone(),
            )
4202
4203
        else:
            out_ret, aux_ctx_tensors = fused_attn_fwd_qkvpacked(
4204
4205
4206
4207
4208
4209
4210
                is_training,
                max_seqlen,
                cu_seqlens,
                qkv,
                qkv_dtype,
                fused_attention_backend,
                attn_bias,
4211
                cu_seqlens_padded,
4212
4213
4214
4215
4216
4217
4218
4219
4220
4221
4222
4223
                None,
                None,
                None,
                None,
                None,
                None,
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
4224
                window_size,
4225
4226
                rng_gen,
            )
4227
4228
4229
4230
4231
            fp8_tensors = (None, None, None, None)
            out_save = out_ret

        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        qkvo_tensors = (qkv, out_save) if not ctx.fp8 else (None, None)
4232
        ctx.save_for_backward(
4233
            *qkvo_tensors, cu_seqlens, cu_seqlens_padded, *fp8_tensors, *aux_ctx_tensors
4234
        )
4235
        ctx.fp8_meta = fp8_meta
4236
4237
4238
4239
4240
4241
4242
4243
        ctx.max_seqlen = max_seqlen
        ctx.qkv_dtype = qkv_dtype
        ctx.attn_scale = attn_scale
        ctx.dropout_p = dropout_p
        ctx.fast_zero_fill = fast_zero_fill
        ctx.qkv_layout = qkv_layout
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_mask_type = attn_mask_type
4244
        ctx.window_size = window_size
4245
        ctx.fused_attention_backend = (
4246
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
4247
        )
4248
        ctx.use_FAv2_bwd = use_FAv2_bwd
4249
        ctx.deterministic = deterministic
4250

4251
        return out_ret
4252
4253
4254

    @staticmethod
    def backward(ctx, d_out):
4255
        if ctx.fp8_meta["recipe"].fp8_mha:
4256
4257
4258
            assert isinstance(
                d_out, Float8Tensor
            ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
4259
4260
4261
            d_out_f8tensor = d_out
            d_out = d_out._data

4262
        d_out = d_out.contiguous()
4263
4264
4265
4266
        (
            qkv,
            out,
            cu_seqlens,
4267
            cu_seqlens_padded,
4268
4269
4270
4271
4272
4273
            qkv_fp8,
            out_fp8,
            fwd_scales,
            fwd_scale_invs,
            *aux_ctx_tensors,
        ) = ctx.saved_tensors
4274
4275
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
4276
        if ctx.use_FAv2_bwd:
4277
            softmax_lse, rng_state = aux_ctx_tensors
4278
4279
            dqkv = torch.empty_like(qkv)
            maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
4280
4281
4282
            d_out, q, k, v, out = [
                maybe_contiguous(x) for x in (d_out, qkv[:, 0], qkv[:, 1], qkv[:, 2], out)
            ]
4283
            flash_attn_cuda_bwd(
4284
4285
4286
4287
4288
4289
4290
4291
4292
4293
4294
4295
4296
4297
4298
4299
4300
4301
4302
                d_out,
                q,
                k,
                v,
                out,
                softmax_lse,
                dqkv[:, 0],
                dqkv[:, 1],
                dqkv[:, 2],
                cu_seqlens,
                cu_seqlens,
                ctx.max_seqlen,
                ctx.max_seqlen,
                ctx.dropout_p,
                ctx.attn_scale,
                False,
                "causal" in ctx.attn_mask_type,
                None,
                rng_state,
4303
            )
4304
            dqkv = dqkv[..., : d_out.shape[-1]]
4305
        else:
4306
4307
            with torch.cuda.nvtx.range("_FusedAttn_qkvpacked"):
                if ctx.fp8:
4308
                    fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True)
4309
                    fp8_dtype_backward = get_fp8_te_dtype(
4310
4311
                        ctx.fp8_meta["recipe"], fprop_tensor=False
                    )
4312
4313
                    if ctx.fp8_meta["recipe"].fp8_mha:
                        d_out_fp8 = d_out
4314
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO] = d_out_f8tensor._scale_inv
4315
4316
4317
                    else:
                        d_out_fp8 = cast_to_fp8(
                            d_out.view(-1, d_out.shape[-2] * d_out.shape[-1]),
4318
4319
4320
4321
                            ctx.fp8_meta["scaling_bwd"],
                            META_DO,
                            fp8_dtype_backward,
                        ).view(d_out.shape)
4322
                    dqkv_fp8, *rest = fused_attn_bwd_qkvpacked(
4323
4324
4325
4326
4327
4328
4329
4330
                        ctx.max_seqlen,
                        cu_seqlens,
                        qkv_fp8,
                        out_fp8,
                        d_out_fp8,
                        fp8_dtype_forward,
                        fp8_dtype_backward,
                        aux_ctx_tensors,
4331
                        ctx.fused_attention_backend,
4332
                        cu_seqlens_padded,
4333
4334
4335
4336
4337
4338
4339
4340
4341
4342
4343
4344
4345
4346
4347
4348
                        fwd_scale_invs[META_QKV],  # d_scale_qkv,
                        fwd_scale_invs[META_S],  # d_scale_s,
                        fwd_scale_invs[META_O],  # d_scale_o,
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO],  # d_scale_do
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DP],  # d_scale_dp
                        fwd_scales[META_S],  # q_scale_s
                        ctx.fp8_meta["scaling_bwd"].scale[META_DP],  # q_scale_dp
                        ctx.fp8_meta["scaling_bwd"].scale[META_DQKV],  # q_scale_dqkv
                        ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DP],  # amax_dp
                        ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DQKV],  # amax_dqkv
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
4349
4350
                        ctx.window_size,
                        ctx.deterministic,
4351
                    )
4352
                    if ctx.fp8_meta["recipe"].fp8_mha:
4353
4354
                        dqkv = Float8Tensor(
                            data=dqkv_fp8,
4355
4356
4357
4358
4359
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
4360
                        )
4361
                    else:
4362
4363
4364
4365
4366
4367
4368
4369
4370
4371
                        dqkv_c_fp8 = dqkv_fp8.view(
                            -1, dqkv_fp8.shape[-3] * dqkv_fp8.shape[-2] * dqkv_fp8.shape[-1]
                        )
                        dqkv = cast_from_fp8(
                            dqkv_c_fp8,
                            ctx.fp8_meta["scaling_bwd"],
                            META_DQKV,
                            fp8_dtype_backward,
                            ctx.qkv_dtype,
                        ).view(dqkv_fp8.shape)
4372
4373
4374
4375
                else:
                    if d_out.dtype == torch.uint8:
                        d_out = d_out_f8tensor.from_float8(qkv.dtype)
                    dqkv, *rest = fused_attn_bwd_qkvpacked(
4376
4377
4378
4379
4380
4381
4382
4383
                        ctx.max_seqlen,
                        cu_seqlens,
                        qkv,
                        out,
                        d_out,
                        ctx.qkv_dtype,
                        ctx.qkv_dtype,
                        aux_ctx_tensors,
4384
                        ctx.fused_attention_backend,
4385
                        cu_seqlens_padded,
4386
4387
4388
4389
4390
4391
4392
4393
4394
4395
4396
4397
4398
4399
4400
4401
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
4402
4403
                        ctx.window_size,
                        ctx.deterministic,
4404
                    )
4405

4406
4407
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
4408
4409
4410
4411
4412
4413
4414
4415
4416
4417
4418
4419
4420
4421
4422
4423
4424
4425
4426
4427
4428
            return (
                None,
                None,
                None,
                None,
                dqkv,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
4429
4430
                None,
                None,
4431
            )
4432
        # else, return (dqkv, dbias)
4433
4434
4435
4436
4437
4438
4439
4440
4441
4442
4443
4444
4445
4446
4447
4448
4449
4450
4451
4452
4453
        return (
            None,
            None,
            None,
            None,
            dqkv,
            None,
            rest[0],
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
4454
4455
            None,
            None,
4456
        )
4457

4458

4459
4460
4461
4462
class FusedAttnFunc_kvpacked(torch.autograd.Function):
    """Function for FusedAttention with packed KV input"""

    @staticmethod
4463
4464
4465
4466
4467
4468
4469
    def forward(
        ctx,
        is_training,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q,
        cu_seqlens_kv,
4470
4471
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
4472
4473
4474
4475
4476
4477
4478
4479
4480
4481
        q,
        kv,
        qkv_dtype,
        attn_bias,
        attn_scale,
        dropout_p,
        fast_zero_fill,
        qkv_layout,
        attn_bias_type,
        attn_mask_type,
4482
        window_size,
4483
4484
4485
4486
4487
        rng_gen,
        fused_attention_backend,
        use_FAv2_bwd,
        fp8,
        fp8_meta,
4488
        deterministic,
4489
    ):
4490
4491
        if fp8:
            if fp8_meta["recipe"].fp8_mha:
4492
4493
4494
                assert isinstance(q, Float8Tensor) and isinstance(
                    kv, Float8Tensor
                ), "q/kv must be Float8Tensors for FP8 MHA."
4495
4496
4497
4498
4499
4500
4501
                fp8_meta["scaling_fwd"].scale_inv[META_QKV] = q._scale_inv
            fused_attention_backend = FusedAttnBackend["FP8"]
            fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
            if fp8_meta["recipe"].fp8_mha:
                q_fp8, kv_fp8 = q._data, kv._data
            else:
                # 1: qkv packed, 2: kv packed, 3: qkv separate
4502
4503
4504
4505
4506
4507
4508
4509
                qkv_group = len(qkv_layout.split("_"))
                assert qkv_group == 2, (
                    "qkv layout should conform to hd_2hd or hd_h2d, e.g. sbhd_sb2hd,              "
                    f"       but found {qkv_layout}."
                )
                q_fp8 = cast_to_fp8(q, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward).view(
                    q.shape
                )
4510
                kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
4511
4512
4513
                kv_fp8 = cast_to_fp8(
                    kv_c, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                ).view(kv.shape)
4514
            out_fp8, aux_ctx_tensors = fused_attn_fwd_kvpacked(
4515
4516
4517
4518
4519
4520
4521
4522
4523
4524
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q_fp8,
                kv_fp8,
                fp8_dtype_forward,
                fused_attention_backend,
                attn_bias,
4525
4526
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
4527
4528
4529
4530
4531
4532
                fp8_meta["scaling_fwd"].scale_inv[META_QKV],
                fp8_meta["scaling_fwd"].scale_inv[META_S],
                fp8_meta["scaling_fwd"].scale[META_S],
                fp8_meta["scaling_fwd"].scale[META_O],
                fp8_meta["scaling_fwd"].amax_history[0][META_S],
                fp8_meta["scaling_fwd"].amax_history[0][META_O],
4533
4534
4535
4536
4537
4538
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
4539
                window_size,
4540
4541
                rng_gen,
            )
4542
            if fp8_meta["recipe"].fp8_mha:
4543
4544
                out_ret = Float8Tensor(
                    data=out_fp8,
4545
4546
4547
4548
4549
4550
4551
4552
4553
                    fp8_meta=fp8_meta,
                    fp8_meta_forward=True,
                    fp8_meta_index=META_O,
                    fp8_dtype=fp8_dtype_forward,
                    dtype=q.dtype,
                )
            else:
                out_ret = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
4554
4555
4556
4557
4558
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    qkv_dtype,
                ).view(out_fp8.shape)
4559
4560
            out_save = out_ret
            if fp8_meta["recipe"].fp8_mha and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
4561
4562
4563
                q = cast_from_fp8(
                    q._data, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward, TE_DType[q.dtype]
                ).view(q.shape)
4564
                kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
4565
4566
                kv = cast_from_fp8(
                    kv_c._data,
4567
                    fp8_meta["scaling_fwd"],
4568
4569
4570
4571
                    META_QKV,
                    fp8_dtype_forward,
                    TE_DType[kv.dtype],
                ).view(kv.shape)
4572
4573
                out_save = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
4574
4575
4576
4577
4578
4579
4580
4581
4582
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    qkv_dtype,
                ).view(out_fp8.shape)
            fp8_tensors = (
                q_fp8,
                kv_fp8,
                out_fp8,
4583
                fp8_meta["scaling_fwd"].scale.clone(),
4584
4585
                fp8_meta["scaling_fwd"].scale_inv.clone(),
            )
4586
4587
        else:
            out_ret, aux_ctx_tensors = fused_attn_fwd_kvpacked(
4588
4589
4590
4591
4592
4593
4594
4595
4596
4597
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q,
                kv,
                qkv_dtype,
                fused_attention_backend,
                attn_bias,
4598
4599
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
4600
4601
4602
4603
4604
4605
4606
4607
4608
4609
4610
4611
                None,
                None,
                None,
                None,
                None,
                None,
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
4612
                window_size,
4613
4614
                rng_gen,
            )
4615
4616
4617
4618
4619
            out_save = out_ret
            fp8_tensors = (None, None, None, None, None)

        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        qkvo_tensors = (q, kv, out_save) if not ctx.fp8 else (None, None, None)
4620
4621
4622
4623
        ctx.save_for_backward(
            *qkvo_tensors,
            cu_seqlens_q,
            cu_seqlens_kv,
4624
4625
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
4626
4627
4628
            *fp8_tensors,
            *aux_ctx_tensors,
        )
4629
        ctx.fp8_meta = fp8_meta
4630
4631
4632
4633
4634
4635
4636
4637
4638
        ctx.max_seqlen_q = max_seqlen_q
        ctx.max_seqlen_kv = max_seqlen_kv
        ctx.qkv_dtype = qkv_dtype
        ctx.attn_scale = attn_scale
        ctx.dropout_p = dropout_p
        ctx.fast_zero_fill = fast_zero_fill
        ctx.qkv_layout = qkv_layout
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_mask_type = attn_mask_type
4639
        ctx.window_size = window_size
4640
        ctx.fused_attention_backend = (
4641
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
4642
        )
4643
        ctx.use_FAv2_bwd = use_FAv2_bwd
4644
        ctx.deterministic = deterministic
4645

4646
        return out_ret
4647
4648
4649

    @staticmethod
    def backward(ctx, d_out):
4650
        if ctx.fp8_meta["recipe"].fp8_mha:
4651
4652
4653
            assert isinstance(
                d_out, Float8Tensor
            ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
4654
4655
4656
            d_out_f8tensor = d_out
            d_out = d_out._data

4657
        d_out = d_out.contiguous()
4658
4659
4660
4661
4662
4663
        (
            q,
            kv,
            out,
            cu_seqlens_q,
            cu_seqlens_kv,
4664
4665
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
4666
4667
4668
4669
4670
4671
4672
            q_fp8,
            kv_fp8,
            out_fp8,
            fwd_scales,
            fwd_scale_invs,
            *aux_ctx_tensors,
        ) = ctx.saved_tensors
4673
4674
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
4675
        if ctx.use_FAv2_bwd:
4676
            softmax_lse, rng_state = aux_ctx_tensors
4677
4678
4679
            dq = torch.empty_like(q)
            dkv = torch.empty_like(kv)
            maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
4680
            d_out, q, k, v, out = [maybe_contiguous(x) for x in (d_out, q, kv[:, 0], kv[:, 1], out)]
4681
            flash_attn_cuda_bwd(
4682
4683
4684
4685
4686
4687
4688
4689
4690
4691
4692
4693
4694
4695
4696
4697
4698
4699
4700
                d_out,
                q,
                k,
                v,
                out,
                softmax_lse,
                dq,
                dkv[:, 0],
                dkv[:, 1],
                cu_seqlens_q,
                cu_seqlens_kv,
                ctx.max_seqlen_q,
                ctx.max_seqlen_kv,
                ctx.dropout_p,
                ctx.attn_scale,
                False,
                "causal" in ctx.attn_mask_type,
                None,
                rng_state,
4701
            )
4702
4703
            dq = dq[..., : d_out.shape[-1]]
            dkv = dkv[..., : d_out.shape[-1]]
4704
        else:
4705
4706
            with torch.cuda.nvtx.range("_FusedAttn_kvpacked"):
                if ctx.fp8:
4707
                    fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True)
4708
                    fp8_dtype_backward = get_fp8_te_dtype(
4709
4710
                        ctx.fp8_meta["recipe"], fprop_tensor=False
                    )
4711
4712
                    if ctx.fp8_meta["recipe"].fp8_mha:
                        d_out_fp8 = d_out
4713
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO] = d_out_f8tensor._scale_inv
4714
4715
4716
                    else:
                        d_out_fp8 = cast_to_fp8(
                            d_out.view(-1, d_out.shape[-2] * d_out.shape[-1]),
4717
4718
4719
4720
                            ctx.fp8_meta["scaling_bwd"],
                            META_DO,
                            fp8_dtype_backward,
                        ).view(d_out.shape)
4721
                    dq_fp8, dkv_fp8, *rest = fused_attn_bwd_kvpacked(
4722
4723
4724
4725
4726
4727
4728
4729
4730
4731
4732
                        ctx.max_seqlen_q,
                        ctx.max_seqlen_kv,
                        cu_seqlens_q,
                        cu_seqlens_kv,
                        q_fp8,
                        kv_fp8,
                        out_fp8,
                        d_out_fp8,
                        fp8_dtype_forward,
                        fp8_dtype_backward,
                        aux_ctx_tensors,
4733
                        ctx.fused_attention_backend,
4734
4735
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
4736
4737
4738
4739
4740
4741
4742
4743
4744
4745
4746
4747
4748
4749
4750
4751
                        fwd_scale_invs[META_QKV],  # d_scale_qkv,
                        fwd_scale_invs[META_S],  # d_scale_s,
                        fwd_scale_invs[META_O],  # d_scale_o,
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO],  # d_scale_do
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DP],  # d_scale_dp
                        fwd_scales[META_S],  # q_scale_s
                        ctx.fp8_meta["scaling_bwd"].scale[META_DP],  # q_scale_dp
                        ctx.fp8_meta["scaling_bwd"].scale[META_DQKV],  # q_scale_dqkv
                        ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DP],  # amax_dp
                        ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DQKV],  # amax_dqkv
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
4752
4753
                        ctx.window_size,
                        ctx.deterministic,
4754
                    )
4755
                    if ctx.fp8_meta["recipe"].fp8_mha:
4756
4757
                        dq = Float8Tensor(
                            data=dq_fp8,
4758
4759
4760
4761
4762
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
4763
4764
4765
                        )
                        dkv = Float8Tensor(
                            data=dkv_fp8,
4766
4767
4768
4769
4770
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
4771
                        )
4772
4773
4774
                    else:
                        dq = cast_from_fp8(
                            dq_fp8.view(-1, dq_fp8.shape[-2] * dq_fp8.shape[-1]),
4775
4776
4777
4778
4779
4780
4781
4782
4783
4784
4785
4786
4787
4788
4789
                            ctx.fp8_meta["scaling_bwd"],
                            META_DQKV,
                            fp8_dtype_backward,
                            ctx.qkv_dtype,
                        ).view(dq_fp8.shape)
                        dkv_c_fp8 = dkv_fp8.view(
                            -1, dkv_fp8.shape[-3] * dkv_fp8.shape[-2] * dkv_fp8.shape[-1]
                        )
                        dkv = cast_from_fp8(
                            dkv_c_fp8,
                            ctx.fp8_meta["scaling_bwd"],
                            META_DQKV,
                            fp8_dtype_backward,
                            ctx.qkv_dtype,
                        ).view(dkv_fp8.shape)
4790
4791
4792
4793
                else:
                    if d_out.dtype == torch.uint8:
                        d_out = d_out_f8tensor.from_float8(q.dtype)
                    dq, dkv, *rest = fused_attn_bwd_kvpacked(
4794
4795
4796
4797
4798
4799
4800
4801
4802
4803
4804
                        ctx.max_seqlen_q,
                        ctx.max_seqlen_kv,
                        cu_seqlens_q,
                        cu_seqlens_kv,
                        q,
                        kv,
                        out,
                        d_out,
                        ctx.qkv_dtype,
                        ctx.qkv_dtype,
                        aux_ctx_tensors,
4805
                        ctx.fused_attention_backend,
4806
4807
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
4808
4809
4810
4811
4812
4813
4814
4815
4816
4817
4818
4819
4820
4821
4822
4823
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
4824
4825
                        ctx.window_size,
                        ctx.deterministic,
4826
                    )
4827

4828
4829
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
4830
4831
4832
4833
4834
4835
4836
4837
4838
4839
4840
4841
4842
4843
4844
4845
4846
4847
4848
4849
4850
4851
4852
4853
4854
            return (
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                dq,
                dkv,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
4855
4856
                None,
                None,
4857
            )
4858
        # else, return (dqkv, dbias)
4859
4860
4861
4862
4863
4864
4865
4866
4867
4868
4869
4870
4871
4872
4873
4874
4875
4876
4877
4878
4879
4880
4881
4882
4883
        return (
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            dq,
            dkv,
            None,
            rest[0],
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
4884
4885
            None,
            None,
4886
4887
        )

4888

4889
4890
4891
4892
class FusedAttnFunc(torch.autograd.Function):
    """Function for FusedAttention with separate Q, K, V tensors"""

    @staticmethod
4893
4894
4895
4896
4897
4898
4899
    def forward(
        ctx,
        is_training,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q,
        cu_seqlens_kv,
4900
4901
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
4902
4903
4904
4905
4906
4907
4908
4909
4910
4911
4912
        q,
        k,
        v,
        qkv_dtype,
        attn_bias,
        attn_scale,
        dropout_p,
        fast_zero_fill,
        qkv_layout,
        attn_bias_type,
        attn_mask_type,
4913
        window_size,
4914
4915
4916
4917
4918
        rng_gen,
        fused_attention_backend,
        use_FAv2_bwd,
        fp8,
        fp8_meta,
4919
        deterministic,
4920
    ):
4921
4922
4923
4924
        if fp8:
            fused_attention_backend = FusedAttnBackend["FP8"]
            fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
            if fp8_meta["recipe"].fp8_mha:
4925
4926
                assert (
                    isinstance(q, Float8Tensor)
4927
                    and isinstance(k, Float8Tensor)
4928
4929
                    and isinstance(v, Float8Tensor)
                ), "q/k/v must be Float8Tensors for FP8 MHA."
4930
4931
4932
4933
                fp8_meta["scaling_fwd"].scale_inv[META_QKV] = q._scale_inv
                q_fp8, k_fp8, v_fp8 = q._data, k._data, v._data
            else:
                # 1: qkv packed, 2: kv packed, 3: qkv separate
4934
                qkv_group = len(qkv_layout.split("_"))
4935
                if qkv_group == 1:
4936
4937
                    dim = qkv_layout.find("3")
                    qkv = _combine_tensors([q, k, v], dim)
4938
                    qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
4939
4940
4941
4942
                    qkv_fp8 = cast_to_fp8(
                        qkv_c, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                    ).view(qkv.shape)
                    q_fp8, k_fp8, v_fp8 = _SplitAlongDim.apply(qkv_fp8, dim, [1, 1, 1])
4943
4944
                    q_fp8, k_fp8, v_fp8 = [x.squeeze(dim) for x in [q_fp8, k_fp8, v_fp8]]
                if qkv_group == 2:
4945
4946
4947
4948
4949
                    q_fp8 = cast_to_fp8(
                        q, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                    ).view(q.shape)
                    dim = qkv_layout.split("_")[1].find("2")
                    kv = _combine_tensors([k, v], dim)
4950
                    kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
4951
4952
4953
4954
                    kv_fp8 = cast_to_fp8(
                        kv_c, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                    ).view(kv.shape)
                    k_fp8, v_fp8 = _SplitAlongDim.apply(kv_fp8, dim, [1, 1])
4955
4956
                    k_fp8, v_fp8 = [x.squeeze(dim) for x in [k_fp8, v_fp8]]
                if qkv_group == 3:
4957
4958
4959
4960
4961
4962
4963
4964
4965
                    q_fp8 = cast_to_fp8(
                        q, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                    ).view(q.shape)
                    k_fp8 = cast_to_fp8(
                        k, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                    ).view(k.shape)
                    v_fp8 = cast_to_fp8(
                        v, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                    ).view(v.shape)
4966
            out_fp8, aux_ctx_tensors = fused_attn_fwd(
4967
4968
4969
4970
4971
4972
4973
4974
4975
4976
4977
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q_fp8,
                k_fp8,
                v_fp8,
                fp8_dtype_forward,
                fused_attention_backend,
                attn_bias,
4978
4979
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
4980
4981
4982
4983
4984
4985
                fp8_meta["scaling_fwd"].scale_inv[META_QKV],
                fp8_meta["scaling_fwd"].scale_inv[META_S],
                fp8_meta["scaling_fwd"].scale[META_S],
                fp8_meta["scaling_fwd"].scale[META_O],
                fp8_meta["scaling_fwd"].amax_history[0][META_S],
                fp8_meta["scaling_fwd"].amax_history[0][META_O],
4986
4987
4988
4989
4990
4991
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
4992
                window_size,
4993
4994
                rng_gen,
            )
4995
            if fp8_meta["recipe"].fp8_mha:
4996
4997
                out_ret = Float8Tensor(
                    data=out_fp8,
4998
4999
5000
5001
5002
5003
5004
5005
5006
                    fp8_meta=fp8_meta,
                    fp8_meta_forward=True,
                    fp8_meta_index=META_O,
                    fp8_dtype=fp8_dtype_forward,
                    dtype=q.dtype,
                )
            else:
                out_ret = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
5007
5008
5009
5010
5011
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    qkv_dtype,
                ).view(out_fp8.shape)
5012
5013
5014
5015
            out_save = out_ret

            if fp8_meta["recipe"].fp8_mha and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                # 1: qkv packed, 2: kv packed, 3: qkv separate
5016
                qkv_group = len(qkv_layout.split("_"))
5017
                if qkv_group == 1:
5018
5019
                    dim = qkv_layout.find("3")
                    qkv = _combine_tensors([q, k, v], dim)
5020
                    qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
5021
5022
                    qkv_no_fp8 = cast_from_fp8(
                        qkv_c._data,
5023
                        fp8_meta["scaling_fwd"],
5024
5025
5026
5027
5028
                        META_QKV,
                        fp8_dtype_forward,
                        TE_DType[qkv.dtype],
                    ).view(qkv.shape)
                    q, k, v = _SplitAlongDim.apply(qkv_no_fp8, dim, [1, 1, 1])
5029
5030
                    q, k, v = [x.squeeze(dim) for x in [q, k, v]]
                if qkv_group == 2:
5031
5032
                    q = cast_from_fp8(
                        q._data,
5033
                        fp8_meta["scaling_fwd"],
5034
5035
5036
5037
5038
5039
                        META_QKV,
                        fp8_dtype_forward,
                        TE_DType[q.dtype],
                    ).view(q.shape)
                    dim = qkv_layout.split("_")[1].find("2")
                    kv = _combine_tensors([k, v], dim)
5040
                    kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
5041
5042
                    kv_no_fp8 = cast_from_fp8(
                        kv_c._data,
5043
                        fp8_meta["scaling_fwd"],
5044
5045
5046
5047
5048
                        META_QKV,
                        fp8_dtype_forward,
                        TE_DType[kv.dtype],
                    ).view(kv.shape)
                    k, v = _SplitAlongDim.apply(kv_no_fp8, dim, [1, 1])
5049
5050
                    k, v = [x.squeeze(dim) for x in [k, v]]
                if qkv_group == 3:
5051
5052
                    q = cast_from_fp8(
                        q._data,
5053
                        fp8_meta["scaling_fwd"],
5054
5055
5056
5057
5058
5059
                        META_QKV,
                        fp8_dtype_forward,
                        TE_DType[q.dtype],
                    ).view(q.shape)
                    k = cast_from_fp8(
                        k._data,
5060
                        fp8_meta["scaling_fwd"],
5061
5062
5063
5064
5065
5066
                        META_QKV,
                        fp8_dtype_forward,
                        TE_DType[k.dtype],
                    ).view(k.shape)
                    v = cast_from_fp8(
                        v._data,
5067
                        fp8_meta["scaling_fwd"],
5068
5069
5070
5071
                        META_QKV,
                        fp8_dtype_forward,
                        TE_DType[v.dtype],
                    ).view(v.shape)
5072
5073
                out_save = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
5074
5075
5076
5077
5078
5079
5080
5081
5082
5083
5084
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    qkv_dtype,
                ).view(out_fp8.shape)

            fp8_tensors = (
                q_fp8,
                k_fp8,
                v_fp8,
                out_fp8,
5085
                fp8_meta["scaling_fwd"].scale.clone(),
5086
5087
                fp8_meta["scaling_fwd"].scale_inv.clone(),
            )
5088
5089
        else:
            out_ret, aux_ctx_tensors = fused_attn_fwd(
5090
5091
5092
5093
5094
5095
5096
5097
5098
5099
5100
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q,
                k,
                v,
                qkv_dtype,
                fused_attention_backend,
                attn_bias,
5101
5102
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
5103
5104
5105
5106
5107
5108
5109
5110
5111
5112
5113
5114
                None,
                None,
                None,
                None,
                None,
                None,
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
5115
                window_size,
5116
5117
                rng_gen,
            )
5118
5119
            out_save = out_ret
            fp8_tensors = (None, None, None, None, None, None)
5120

5121
        from .cpu_offload import CPUOffloadEnabled
5122

5123
        if CPUOffloadEnabled:
5124
            tensor_list = [q, k, v, out_save, cu_seqlens_q, cu_seqlens_kv]
5125
            qkv_layout = "sbhd_sbhd_sbhd"
5126
5127
5128
5129
            for tensor in tensor_list:
                if tensor is not None:
                    tensor.activation_offloading = True

5130
5131
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        qkvo_tensors = (q, k, v, out_save) if not ctx.fp8 else (None, None, None, None)
5132
5133
5134
5135
        ctx.save_for_backward(
            *qkvo_tensors,
            cu_seqlens_q,
            cu_seqlens_kv,
5136
5137
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
5138
5139
5140
            *fp8_tensors,
            *aux_ctx_tensors,
        )
5141
        ctx.fp8_meta = fp8_meta
5142
5143
5144
5145
5146
5147
5148
5149
5150
        ctx.max_seqlen_q = max_seqlen_q
        ctx.max_seqlen_kv = max_seqlen_kv
        ctx.qkv_dtype = qkv_dtype
        ctx.attn_scale = attn_scale
        ctx.dropout_p = dropout_p
        ctx.fast_zero_fill = fast_zero_fill
        ctx.qkv_layout = qkv_layout
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_mask_type = attn_mask_type
5151
        ctx.window_size = window_size
5152
        ctx.fused_attention_backend = (
5153
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
5154
        )
5155
        ctx.use_FAv2_bwd = use_FAv2_bwd
5156
        ctx.deterministic = deterministic
5157

5158
        return out_ret
5159
5160
5161

    @staticmethod
    def backward(ctx, d_out):
5162
        if ctx.fp8_meta["recipe"].fp8_mha:
5163
5164
5165
            assert isinstance(
                d_out, Float8Tensor
            ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
5166
5167
5168
            d_out_f8tensor = d_out
            d_out = d_out._data

5169
        d_out = d_out.contiguous()
5170
5171
5172
5173
5174
5175
5176
        (
            q,
            k,
            v,
            out,
            cu_seqlens_q,
            cu_seqlens_kv,
5177
5178
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
5179
5180
5181
5182
5183
5184
5185
5186
            q_fp8,
            k_fp8,
            v_fp8,
            out_fp8,
            fwd_scales,
            fwd_scale_invs,
            *aux_ctx_tensors,
        ) = ctx.saved_tensors
5187
5188
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
5189
        if ctx.use_FAv2_bwd:
5190
            softmax_lse, rng_state = aux_ctx_tensors
5191
5192
5193
5194
            dq = torch.empty_like(q)
            dk = torch.empty_like(k)
            dv = torch.empty_like(v)
            maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
5195
            d_out, q, k, v, out = [maybe_contiguous(x) for x in (d_out, q, k, v, out)]
5196
            flash_attn_cuda_bwd(
5197
5198
5199
5200
5201
5202
5203
5204
5205
5206
5207
5208
5209
5210
5211
5212
5213
5214
5215
                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,
5216
            )
5217
5218
5219
            dq = dq[..., : d_out.shape[-1]]
            dk = dk[..., : d_out.shape[-1]]
            dv = dv[..., : d_out.shape[-1]]
5220
        else:
5221
5222
5223
5224
            with torch.cuda.nvtx.range("_FusedAttn"):
                if ctx.fp8:
                    fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True)
                    fp8_dtype_backward = get_fp8_te_dtype(
5225
5226
                        ctx.fp8_meta["recipe"], fprop_tensor=False
                    )
5227
5228
                    if ctx.fp8_meta["recipe"].fp8_mha:
                        d_out_fp8 = d_out
5229
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO] = d_out_f8tensor._scale_inv
5230
5231
5232
                    else:
                        d_out_fp8 = cast_to_fp8(
                            d_out.view(-1, d_out.shape[-2] * d_out.shape[-1]),
5233
5234
5235
5236
                            ctx.fp8_meta["scaling_bwd"],
                            META_DO,
                            fp8_dtype_backward,
                        ).view(d_out.shape)
5237
                    dq_fp8, dk_fp8, dv_fp8, *rest = fused_attn_bwd(
5238
5239
5240
5241
5242
5243
5244
5245
5246
5247
5248
5249
                        ctx.max_seqlen_q,
                        ctx.max_seqlen_kv,
                        cu_seqlens_q,
                        cu_seqlens_kv,
                        q_fp8,
                        k_fp8,
                        v_fp8,
                        out_fp8,
                        d_out_fp8,
                        fp8_dtype_forward,
                        fp8_dtype_backward,
                        aux_ctx_tensors,
5250
                        ctx.fused_attention_backend,
5251
5252
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
5253
5254
5255
5256
5257
5258
5259
5260
5261
5262
5263
5264
5265
5266
5267
5268
                        fwd_scale_invs[META_QKV],  # d_scale_qkv,
                        fwd_scale_invs[META_S],  # d_scale_s,
                        fwd_scale_invs[META_O],  # d_scale_o,
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO],  # d_scale_do
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DP],  # d_scale_dp
                        fwd_scales[META_S],  # q_scale_s
                        ctx.fp8_meta["scaling_bwd"].scale[META_DP],  # q_scale_dp
                        ctx.fp8_meta["scaling_bwd"].scale[META_DQKV],  # q_scale_dqkv
                        ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DP],  # amax_dp
                        ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DQKV],  # amax_dqkv
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
5269
5270
                        ctx.window_size,
                        ctx.deterministic,
5271
                    )
5272

5273
                    if ctx.fp8_meta["recipe"].fp8_mha:
5274
5275
                        dq = Float8Tensor(
                            data=dq_fp8,
5276
5277
5278
5279
5280
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
5281
5282
5283
                        )
                        dk = Float8Tensor(
                            data=dk_fp8,
5284
5285
5286
5287
5288
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
5289
5290
5291
                        )
                        dv = Float8Tensor(
                            data=dv_fp8,
5292
5293
5294
5295
5296
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
5297
                        )
5298
                    else:
5299
                        qkv_group = len(ctx.qkv_layout.split("_"))
5300
                        if qkv_group == 1:
5301
5302
5303
5304
5305
5306
5307
5308
5309
5310
5311
5312
5313
                            dim = ctx.qkv_layout.find("3")
                            dqkv_fp8 = _combine_tensors([dq_fp8, dk_fp8, dv_fp8], dim)
                            dqkv_c_fp8 = dqkv_fp8.view(
                                -1, dqkv_fp8.shape[-3] * dqkv_fp8.shape[-2] * dqkv_fp8.shape[-1]
                            )
                            dqkv = cast_from_fp8(
                                dqkv_c_fp8,
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dqkv_fp8.shape)
                            dq, dk, dv = _SplitAlongDim.apply(dqkv, dim, [1, 1, 1])
5314
5315
5316
5317
                            dq, dk, dv = [x.squeeze(dim) for x in [dq, dk, dv]]
                        if qkv_group == 2:
                            dq = cast_from_fp8(
                                dq_fp8.view(-1, dq_fp8.shape[-2] * dq_fp8.shape[-1]),
5318
5319
5320
5321
5322
5323
5324
5325
5326
5327
5328
5329
5330
5331
5332
5333
5334
5335
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dq_fp8.shape)
                            dim = ctx.qkv_layout.split("_")[1].find("2")
                            dkv_fp8 = _combine_tensors([dk_fp8, dv_fp8], dim)
                            dkv_c_fp8 = dkv_fp8.view(
                                -1, dkv_fp8.shape[-3] * dkv_fp8.shape[-2] * dkv_fp8.shape[-1]
                            )
                            dkv = cast_from_fp8(
                                dkv_c_fp8,
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dkv_fp8.shape)
                            dk, dv = _SplitAlongDim.apply(dkv, dim, [1, 1])
5336
5337
5338
5339
                            dk, dv = [x.squeeze(dim) for x in [dk, dv]]
                        if qkv_group == 3:
                            dq = cast_from_fp8(
                                dq_fp8.view(-1, dq_fp8.shape[-2] * dq_fp8.shape[-1]),
5340
5341
5342
5343
5344
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dq_fp8.shape)
5345
5346
                            dk = cast_from_fp8(
                                dk_fp8.view(-1, dk_fp8.shape[-2] * dk_fp8.shape[-1]),
5347
5348
5349
5350
5351
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dk_fp8.shape)
5352
5353
                            dv = cast_from_fp8(
                                dv_fp8.view(-1, dv_fp8.shape[-2] * dv_fp8.shape[-1]),
5354
5355
5356
5357
5358
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dv_fp8.shape)
5359
5360
5361
5362
                else:
                    if d_out.dtype == torch.uint8:
                        d_out = d_out_f8tensor.from_float8(q.dtype)
                    dq, dk, dv, *rest = fused_attn_bwd(
5363
5364
5365
5366
5367
5368
5369
5370
5371
5372
5373
5374
                        ctx.max_seqlen_q,
                        ctx.max_seqlen_kv,
                        cu_seqlens_q,
                        cu_seqlens_kv,
                        q,
                        k,
                        v,
                        out,
                        d_out,
                        ctx.qkv_dtype,
                        ctx.qkv_dtype,
                        aux_ctx_tensors,
5375
                        ctx.fused_attention_backend,
5376
5377
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
5378
5379
5380
5381
5382
5383
5384
5385
5386
5387
5388
5389
5390
5391
5392
5393
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
5394
5395
                        ctx.window_size,
                        ctx.deterministic,
5396
                    )
5397

5398
5399
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
5400
5401
5402
5403
5404
5405
5406
5407
5408
5409
5410
5411
5412
5413
5414
5415
5416
5417
5418
5419
5420
5421
5422
5423
5424
5425
            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,
5426
5427
                None,
                None,
5428
            )
5429
        # else, return (dqkv, dbias)
5430
5431
5432
5433
5434
5435
5436
5437
5438
5439
5440
5441
5442
5443
5444
5445
5446
5447
5448
5449
5450
5451
5452
5453
5454
5455
        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,
5456
5457
            None,
            None,
5458
        )
5459

5460

5461
class FusedAttention(torch.nn.Module):
5462
5463
5464
5465
5466
5467
5468
5469
5470
    """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:

5471
5472
5473
5474
    | backend       | 1                       | 2                              |
    | flash based   | no                      | yes                            |
    | cuDNN based   | yes                     | yes                            |
    | qkv dtype     | fp16/bf16               | fp16/bf16                      |
5475
    | attn_type     | self/cross              | self/cross                     |
5476
    | qkv_layout    |                         |                                |
5477
    |  - (q,k,v)    | sb3hd, bs3hd            | sb3hd, bs3hd, sbh3d, bsh3d     |
5478
    |               | sbhd_sb2hd, bshd_bs2hd  | sbhd_sb2hd, bshd_bs2hd         |
5479
5480
    |               | bshd_bshd_bshd          | sbhd_sbh2d, bshd_bsh2d         |
    |               |                         | sbhd_sbhd_sbhd, bshd_bshd_bshd |
5481
5482
    | mask_type     | causal/padding/no_mask  | causal/padding/no_mask         |
    | bias_type     | post_scale_bias/no_bias | post_scale_bias/alibi/no_bias  |
5483
    | dropout       | yes                     | yes                            |
5484
5485
    | max_seqlen    | <=512, multiple of 64   | any, multiple of 64            |
    | head_dim      | 64                      | <=128, multiple of 8           |
5486
    | output dtype  | fp16/bf16               | fp16/bf16                      |
5487
5488
5489
5490
    """

    def __init__(
        self,
5491
        softmax_scale: float,
5492
5493
5494
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
        attention_type: str = "self",
5495
5496
        layer_number: Optional[int] = None,
        deterministic: bool = False,
5497
5498
5499
    ) -> None:
        super().__init__()

5500
        self.softmax_scale = softmax_scale
5501
5502
5503
        self.attention_dropout = attention_dropout
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_type = attention_type
5504
5505
5506
        self.use_FAv2_bwd = os.getenv(
            "NVTE_FUSED_ATTN_USE_FAv2_BWD", "0"
        ) == "1" and get_device_compute_capability() == (9, 0)
5507
        self.layer_number = 1 if layer_number is None else layer_number
5508
        self.deterministic = deterministic
5509

5510
        def remove_extra_states_check(self, incompatible_keys):  # pylint: disable=unused-argument
5511
5512
            """
            Temporarily remove fused_attention._extra_state as a missing key
5513
5514
5515
5516
            or an unexpected key when loading TransformerEngine checkpoints.
            Please store FP8 metadata as DotProductAttention's _extra_state,
            rather than FusedAttention's _extra_state. This hook will be
            phased out in TransformerEngine 2.0.
5517
5518
            """
            for key in incompatible_keys.missing_keys:
5519
                if "fused_attention._extra_state" in key:
5520
                    incompatible_keys.missing_keys.remove(key)
5521
5522
5523
5524
5525
5526
5527
            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."
                    )
5528

5529
5530
        self.register_load_state_dict_post_hook(remove_extra_states_check)

5531
    @no_torch_dynamo()
5532
5533
5534
5535
5536
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
5537
5538
5539
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
5540
5541
        cu_seqlens_q_padded: Optional[torch.Tensor] = None,
        cu_seqlens_kv_padded: Optional[torch.Tensor] = None,
5542
5543
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
5544
        attn_mask_type: str = "causal",
5545
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
5546
        window_size: Optional[Tuple[int, int]] = None,
5547
        fused_attention_backend: tex.NVTE_Fused_Attn_Backend = tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend,
5548
5549
5550
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
        fast_zero_fill: bool = True,
5551
5552
5553
        cp_group: Optional[dist_group_type] = None,
        cp_global_ranks: List[int] = None,
        cp_stream: torch.cuda.Stream = None,
5554
        cp_comm_type: str = "p2p",
5555
5556
        fp8: bool = False,
        fp8_meta: Optional[Dict[str, Any]] = None,
5557
5558
    ) -> torch.Tensor:
        """fused attention fprop"""
5559
5560
5561
        assert (
            fused_attention_backend != tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend
        ), "No fused attention backend supports this input combination!"
5562
        assert (
5563
5564
5565
            (query_layer.dtype in [torch.float16, torch.bfloat16, torch.uint8])
            and (key_layer.dtype in [torch.float16, torch.bfloat16, torch.uint8])
            and (value_layer.dtype in [torch.float16, torch.bfloat16, torch.uint8])
5566
        ), "FusedAttention only supports FP16 and BF16 data types."
5567
5568
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
5569
        ), "FusedAttention only supports CUDA tensors."
5570
5571
        assert (
            qkv_layout in QKVLayouts
5572
        ), f"FusedAttention does not support qkv_layout = {qkv_layout}!"
5573

5574
5575
        cp_size = 1 if cp_group is None else get_distributed_world_size(cp_group)
        context_parallel = cp_size > 1
5576

5577
        qkv_format = "".join([i for i in qkv_layout.split("_")[0] if i.isalpha()])
5578

5579
5580
        if qkv_format in ["sbhd", "bshd"]:
            if qkv_format == "sbhd":
5581
                batch_size, max_seqlen_q, max_seqlen_kv = (
5582
5583
5584
5585
5586
                    query_layer.shape[1],
                    query_layer.shape[0],
                    key_layer.shape[0],
                )
            if qkv_format == "bshd":
5587
                batch_size, max_seqlen_q, max_seqlen_kv = (
5588
5589
5590
5591
                    query_layer.shape[0],
                    query_layer.shape[1],
                    key_layer.shape[1],
                )
5592
5593
            max_seqlen_q *= cp_size
            max_seqlen_kv *= cp_size
5594
            if "padding" in attn_mask_type:
5595
5596
                assert not context_parallel, "Padding mask not supported with context parallelism!"

5597
5598
5599
5600
5601
                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!"
                        )
5602
                    if self.attention_type == "self":
5603
5604
                        cu_seqlens_q = get_cu_seqlens(attention_mask)
                        cu_seqlens_kv = cu_seqlens_q
5605
                    else:
5606
5607
                        cu_seqlens_q = get_cu_seqlens(attention_mask[0])
                        cu_seqlens_kv = get_cu_seqlens(attention_mask[1])
5608
            else:
5609
5610
5611
5612
5613
5614
5615
5616
5617
5618
5619
5620
                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,
                    )
5621
5622
5623
        if qkv_format == "thd":
            assert (
                max_seqlen_q is not None
5624
5625
5626
                and max_seqlen_kv is not None
                and cu_seqlens_q is not None
                and cu_seqlens_kv is not None
5627
            ), "max_seqlen_q/kv and cu_seqlens_q/kv can not be None when qkv_format is thd!"
5628
5629
5630
5631

        if cu_seqlens_q_padded is None or cu_seqlens_kv_padded is None:
            cu_seqlens_q_padded = cu_seqlens_q
            cu_seqlens_kv_padded = cu_seqlens_kv
5632
5633
5634

        qkv_dtype = TE_DType[query_layer.dtype]

5635
5636
5637
5638
5639
        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)
        )
5640
5641

        if context_parallel:
5642
            assert (
5643
5644
5645
5646
5647
5648
5649
5650
                fused_attention_backend == tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen
            ), 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)
            ]
5651
5652
5653
            with self.attention_dropout_ctx():
                output = attn_forward_func_with_cp(
                    self.training,
5654
5655
5656
5657
5658
5659
5660
                    query_layer,
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
5661
5662
                    cu_seqlens_q_padded,
                    cu_seqlens_kv_padded,
5663
                    self.attention_dropout if self.training else 0.0,
5664
5665
5666
                    cp_group,
                    cp_global_ranks,
                    cp_stream,
5667
                    cp_comm_type,
5668
                    softmax_scale=self.softmax_scale,
5669
                    qkv_format=qkv_format,
5670
                    attn_mask_type=attn_mask_type,
5671
5672
                    attn_bias_type=core_attention_bias_type,
                    attn_bias=core_attention_bias,
5673
                    use_fused_attention=True,
5674
                    window_size=window_size,
5675
5676
                )
        else:
5677
5678
5679
5680
5681
            with self.attention_dropout_ctx():
                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!"
5682
                    )
5683
5684
5685
5686
5687
5688
5689
5690
5691
                    assert (
                        fp8_meta is not None
                    ), "FP8 metadata fp8_meta is required for FP8 attention!"
                output = FusedAttnFunc.apply(
                    self.training,
                    max_seqlen_q,
                    max_seqlen_kv,
                    cu_seqlens_q,
                    cu_seqlens_kv,
5692
5693
                    cu_seqlens_q_padded,
                    cu_seqlens_kv_padded,
5694
5695
5696
5697
5698
5699
5700
5701
5702
5703
5704
                    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,
5705
                    window_size,
5706
5707
5708
5709
5710
                    None,  # rng_gen
                    fused_attention_backend,
                    use_FAv2_bwd,
                    fp8,
                    fp8_meta,
5711
                    self.deterministic,
5712
                )
5713

5714
5715
        # ...hd -> ...(hd)
        return output.view(*output.shape[:-2], -1)
5716
5717


5718
class DotProductAttention(TransformerEngineBaseModule):
5719
5720
5721
5722
5723
5724
    """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::

5725
        Argument :attr:`attention_mask` in the `forward` call is only used when
5726
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
5727
5728
5729

    .. warning::

5730
        FlashAttention uses a non-deterministic algorithm for optimal performance. To observe
5731
        deterministic behavior at the cost of performance, use FlashAttention version >= `2.4.1`
5732
5733
        and set the environment variable :attr:`NVTE_ALLOW_NONDETERMINISTIC_ALGO=0`. In order
        to disable`flash-attn` entirely, set :attr:`NVTE_FLASH_ATTN=0`.
5734
5735
5736
5737
5738

    Parameters
    ----------
    num_attention_heads : int
                         number of attention heads in the transformer layer.
5739
5740
5741
    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.
5742
5743
5744
5745
5746
5747
5748
5749
    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`.
5750
5751
    attention_dropout: float, default = 0.0
                      dropout probability for the dropout op during multi-head attention.
5752
    attn_mask_type: str, default = `causal`
5753
                   type of attention mask passed into softmax operation, options are "`no_mask`",
5754
5755
5756
5757
5758
5759
5760
5761
5762
5763
5764
5765
5766
5767
5768
5769
5770
5771
5772
5773
5774
5775
5776
5777
                   "`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
                   "`padding_causal`" and "`padding_causal_bottom_right`", TransformerEngine
                   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].
5778
5779
5780
5781
    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
5782
5783
5784
                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
5785
                be overridden by :attr:`window_size` in `forward` as well.
5786
5787
    attention_type: str, default = `self`
                   type of attention, either "`self`" and "`cross`".
5788
5789
5790
    layer_number: int, default = `None`
                 layer number of the current `DotProductAttention` when multiple such modules
                 are concatenated, for instance in consecutive transformer blocks.
5791
5792
5793
    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,
5794
               `h` the number of heads, `d` head size, and `t` the total number of tokens
5795
5796
5797
5798
5799
               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.
5800
               For that, please use `get_qkv_layout` to gain the layout information.
5801
5802
    softmax_scale: Optional[float], default = `None`
                softmax scale for the attention scores. If `None`, defaults to
5803
                `1.0/math.sqrt(kv_channels if isinstance(kv_channels, int) else kv_channels[0])`.
5804
5805
5806
5807
5808
5809
5810
5811
5812

    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.
5813
5814
5815
5816
5817
5818
5819
5820
5821
    cp_group : ProcessGroup, default = `None`
              context parallel process group.
    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.
5822
5823
5824
    cp_comm_type : str
                  inter-gpu communication type for context parallelism.
                  Can be "p2p" or "all_gather".
5825
5826
5827
5828
5829
    """

    def __init__(
        self,
        num_attention_heads: int,
5830
        kv_channels: Union[int, Tuple[int, int]],
5831
        num_gqa_groups: Optional[int] = None,
5832
        attention_dropout: float = 0.0,
5833
        qkv_format: str = "sbhd",
5834
        attn_mask_type: str = "causal",
5835
        window_size: Optional[Tuple[int, int]] = None,
5836
5837
5838
5839
5840
        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,
5841
        attention_type: str = "self",
5842
        cp_group: Optional[dist_group_type] = None,
5843
        cp_global_ranks: List[int] = None,
5844
        cp_stream: torch.cuda.Stream = None,
5845
        cp_comm_type: str = "p2p",
5846
        softmax_scale: Optional[float] = None,
5847
5848
5849
    ) -> None:
        super().__init__()

5850
        self.logger = logging.getLogger("DotProductAttention")
5851
5852
5853
        self.logger.setLevel(_log_level)
        if not self.logger.hasHandlers():
            self.logger.addHandler(_stream_handler)
5854
        self.qkv_format = qkv_format
5855
        attn_mask_type = attn_mask_type.replace(",", "_")
5856
5857
        if attn_mask_type == "causal_padding":
            attn_mask_type = "padding_causal"
5858
        self.attn_mask_type = attn_mask_type
5859
        self.window_size = check_set_window_size(attn_mask_type, window_size)
5860
5861
5862
5863
5864
5865
5866
        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)
5867
        self.get_rng_state_tracker = get_rng_state_tracker
5868
        self.num_attention_heads = num_attention_heads
5869
        self.layer_number = 1 if layer_number is None else layer_number
5870
5871
5872
        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream
5873
        self.cp_comm_type = cp_comm_type
5874

5875
5876
5877
5878
5879
5880
        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]
        )
5881

5882
        self.num_gqa_groups = num_attention_heads if num_gqa_groups is None else num_gqa_groups
5883
        self.num_gqa_groups_per_partition = int(self.num_gqa_groups // self.tp_size)
5884

5885
5886
5887
        assert (
            num_attention_heads % self.num_gqa_groups == 0
        ), "The number of attention heads must be divisible by the number of GQA groups!"
5888

5889
        self.rng_states_tracker = None
5890
5891
5892
        if sequence_parallel or get_rng_state_tracker is None:
            attention_dropout_ctx = nullcontext
        else:
5893
5894
5895
            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
5896

5897
        if softmax_scale is None:
5898
5899
5900
            softmax_scale = 1.0 / math.sqrt(
                kv_channels if isinstance(kv_channels, int) else kv_channels[0]
            )
5901

5902
5903
5904
        self.deterministic = (
            not bool(int(os.getenv("NVTE_ALLOW_NONDETERMINISTIC_ALGO", "1")))
            or torch.are_deterministic_algorithms_enabled()
5905
        )
5906
5907
5908
5909
5910
5911
5912
5913
5914
5915
5916
5917
5918
5919
5920
5921
5922
5923
5924
        # 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"
5925

5926
        assert attention_type in AttnTypes, f"attention_type {attention_type} not supported"
5927
5928
5929
5930

        self.attention_type = attention_type
        self.attention_dropout = attention_dropout

5931
5932
5933
5934
5935
        attn_kwargs = {
            "attention_dropout": attention_dropout,
            "attention_dropout_ctx": attention_dropout_ctx,
        }

5936
5937
5938
5939
5940
5941
5942
        self.flash_attention = FlashAttention(
            softmax_scale,
            attention_type=attention_type,
            layer_number=layer_number,
            deterministic=self.deterministic,
            **attn_kwargs,
        )
5943

5944
        # Instantiating three types since use of flash-attn and FusedAttention
5945
        # might be ruled out due to forward inputs.
5946
5947
5948
5949
5950
5951
5952
        self.fused_attention = FusedAttention(
            softmax_scale,
            attention_type=attention_type,
            layer_number=layer_number,
            deterministic=self.deterministic,
            **attn_kwargs,
        )
5953

5954
        self.unfused_attention = UnfusedDotProductAttention(
5955
5956
            softmax_scale, **attn_kwargs, layer_number=layer_number
        )
5957

5958
5959
5960
5961
5962
5963
5964
5965
5966
5967
5968
5969
        def remove_extra_states_check(self, incompatible_keys):  # pylint: disable=unused-argument
            """
            Temporarily remove core_attention._extra_state as a missing key
            when loading older TransformerEngine checkpoints. Will phase out
            this hook in TransformerEngine 2.0.
            """
            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)

5970
5971
5972
5973
    def _checkpointed_attention_forward(
        self,
        attention_func: Callable,
        *forward_args: Tuple[torch.Tensor, ...],
5974
        **forward_kwargs: Dict[str, Any],
5975
5976
5977
    ) -> torch.Tensor:
        """Forward method with activation checkpointing."""

5978
5979
        def custom_forward(*input_args, **input_kwargs):
            return attention_func(*input_args, **input_kwargs)
5980
5981
5982

        hidden_states = checkpoint(
            custom_forward,
5983
5984
5985
            distribute_saved_activations=False,
            get_rng_state_tracker=self.get_rng_state_tracker,
            tp_group=self.tp_group,
5986
            *forward_args,
5987
            **forward_kwargs,
5988
5989
5990
5991
        )

        return hidden_states

5992
5993
5994
5995
5996
    def set_context_parallel_group(
        self,
        cp_group: Union[dist_group_type, None],
        cp_global_ranks: List[int],
        cp_stream: torch.cuda.Stream,
5997
        cp_comm_type: str = "p2p",
5998
    ) -> None:
5999
6000
6001
6002
6003
6004
6005
6006
6007
6008
6009
6010
        """
        Set the context parallel attributes for the given
        module before executing the forward pass.

        Parameters
        ----------
        cp_group : ProcessGroup
                  context parallel process group.
        cp_global_ranks : List[int]
                         list of global ranks in the context group.
        cp_stream : torch.cuda.Stream
                   cuda stream for context parallel execution.
6011
6012
6013
        cp_comm_type : str
                      inter-gpu communication type for context parallelism.
                      Can be "p2p" or "all_gather".
6014
        """
6015
6016
6017
        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream
6018
        self.cp_comm_type = cp_comm_type
6019

6020
    @no_torch_dynamo(recursive=False)
6021
6022
6023
6024
6025
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
6026
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
6027
6028
6029
        qkv_format: Optional[str] = None,
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
6030
6031
        cu_seqlens_q_padded: Optional[torch.Tensor] = None,
        cu_seqlens_kv_padded: Optional[torch.Tensor] = None,
6032
6033
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
6034
        attn_mask_type: Optional[str] = None,
6035
        window_size: Optional[Tuple[int, int]] = None,
6036
        checkpoint_core_attention: bool = False,
6037
6038
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
6039
        alibi_slopes: Optional[torch.Tensor] = None,
6040
        fast_zero_fill: bool = True,
6041
        inference_params: Optional[InferenceParams] = None,
6042
        is_first_microbatch: Optional[bool] = None,
6043
6044
6045
6046
6047
6048
    ) -> torch.Tensor:
        """
        Dot Product Attention Layer.

        .. note::

6049
6050
            Argument :attr:`attention_mask` is only used when :attr:`attn_mask_type`
            includes '"padding"' or `"arbitrary"`.
6051

6052
6053
        .. note::

6054
6055
6056
6057
6058
6059
6060
6061
6062
6063
6064
6065
6066
6067
6068
6069
6070
6071
            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,
            and FusedAttention backend if applicable, to use. TransformerEngine prioritizes
            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
6072
6073
6074
6075
6076
            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
            optimizations in FusedAttention. When unset, TransformerEngine determines the code path
            based on its internal logic. These optimizations trade memory for performance
            and should be used with care.
6077

6078
6079
6080
6081
6082
6083
6084
6085
        Parameters
        ----------
        query_layer : torch.Tensor
                     Query tensor.
        key_layer : torch.Tensor
                   Key tensor.
        value_layer : torch.Tensor
                     Value tensor.
6086
6087
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]],
             default = `None`. Boolean tensor(s) used to mask out attention softmax input.
6088
             It should be `None` for causal masks and "`no_mask`". For padding masks, it should be
6089
6090
             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]
6091
6092
6093
6094
             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.
6095
6096
6097
        qkv_format: str, default = `None`
                   If provided, overrides :attr:`qkv_format` from initialization.
        cu_seqlens_q: Optional[torch.Tensor], default = `None`
6098
                   Cumulative sum of sequence lengths (without offset) in a batch for `query_layer`,
6099
6100
                   with shape [batch_size + 1] and dtype torch.int32.
        cu_seqlens_kv: Optional[torch.Tensor], default = `None`
6101
6102
6103
6104
6105
6106
6107
6108
6109
6110
6111
6112
                   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.
        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`.
        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`.
6113
6114
6115
6116
6117
6118
        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.
6119
6120
6121
6122
6123
6124
6125
        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.
6126
        window_size: Optional[Tuple[int, int]], default = `None`
6127
                    Sliding window size for local attention.
6128
6129
6130
6131
6132
        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.
6133
        core_attention_bias_type: str, default = `no_bias`
6134
                    Bias type, {`no_bias`, `pre_scale_bias`, `post_scale_bias`, `alibi`}
6135
        core_attention_bias: Optional[torch.Tensor], default = `None`
6136
6137
                    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.
6138
6139
6140
6141
        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.
6142
        fast_zero_fill: bool, default = `True`
6143
                    Whether to use the fast path to set output tensors to 0 or not.
6144
6145
6146
6147
6148
6149
6150
6151
6152
6153
        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.
6154
6155
6156
6157
6158
6159
6160
6161
6162
6163
6164
6165
6166
        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)
6167
        """
6168
6169
6170
6171
6172
6173
6174
6175
6176
6177
6178
        with self.prepare_forward(
            query_layer,
            is_first_microbatch,
            num_gemms=3,
            allow_non_contiguous=True,
        ) as query_layer:

            if self.fp8:
                if self.fp8_meta["recipe"].fp8_mha:
                    if not self.fp8_meta["recipe"].fp8_dpa:
                        self.fp8_meta["recipe"].fp8_dpa = True
6179
                        self.logger.warning(
6180
6181
6182
                            """Forcing fp8_meta["recipe"].fp8_dpa=True due to """
                            """fp8_meta["recipe"].fp8_mha=True"""
                        )
6183
6184
6185
6186
6187
6188
6189
6190
6191
6192
6193

            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."""
6194

6195
6196
6197
            assert (
                query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
            ), "DotProductAttention only supports CUDA tensors."
6198
6199
6200
            assert (
                query_layer.dtype == key_layer.dtype and query_layer.dtype == value_layer.dtype
            ), "Queries, keys and values must have the same data type!"
6201
6202
6203
            assert (
                key_layer.shape[:-1] == value_layer.shape[:-1]
            ), "Keys and values must have the same batch size, sequence length and number of heads!"
6204
6205
6206
6207
6208
6209
6210
6211
            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}!"
6212

6213
6214
6215
6216
6217
6218
            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"
6219
            assert (
6220
6221
6222
6223
6224
6225
                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!"
6226

6227
6228
6229
6230
            if window_size is None:
                window_size = self.window_size
            window_size = check_set_window_size(attn_mask_type, window_size)

6231
6232
6233
6234
6235
6236
6237
            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."
6238

6239
6240
            if qkv_format is None:
                qkv_format = self.qkv_format
6241

6242
6243
            if inference_params is not None:
                assert self.layer_number is not None, "Layer number must be set!"
6244

6245
6246
6247
                if qkv_format == "bshd":
                    key_layer = key_layer.transpose(0, 1)
                    value_layer = value_layer.transpose(0, 1)
6248

6249
6250
6251
6252
                (
                    inference_key_memory,
                    inference_value_memory,
                ) = inference_params.key_value_memory_dict[self.layer_number]
6253

6254
6255
6256
                batch_start = inference_params.batch_size_offset
                batch_end = batch_start + key_layer.size(1)
                assert batch_end <= inference_key_memory.size(1)
6257

6258
6259
6260
                sequence_start = inference_params.sequence_len_offset
                sequence_end = sequence_start + key_layer.size(0)
                assert sequence_end <= inference_key_memory.size(0)
6261

6262
6263
6264
6265
6266
6267
6268
6269
6270
                # 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, ...]
6271

6272
6273
6274
                if qkv_format == "bshd":
                    key_layer = key_layer.transpose(0, 1)
                    value_layer = value_layer.transpose(0, 1)
6275

6276
6277
                key_layer = key_layer.contiguous()
                value_layer = value_layer.contiguous()
6278
6279

            assert (
6280
6281
6282
6283
6284
6285
6286
6287
6288
6289
                key_layer.shape[-2] == self.num_gqa_groups_per_partition
                and value_layer.shape[-2] == self.num_gqa_groups_per_partition
            ), f"Keys and values must have num_gqa_group = {self.num_gqa_groups} heads!"
            assert qkv_format in [
                "sbhd",
                "bshd",
                "thd",
            ], "DotProductAttention only supports qkv_format = {'sbhd', 'bshd', 'thd'}!"

            if qkv_format == "thd":
6290
                assert all(
6291
6292
6293
6294
6295
6296
6297
6298
6299
6300
6301
6302
6303
6304
                    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!"
                if max_seqlen_q is None:
6305
6306
6307
6308
                    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]
6309
                    max_seqlen_q = int((seqlens_q.max().item() + 63) // 64 * 64)
6310
                if max_seqlen_kv is None:
6311
6312
6313
6314
                    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]
6315
                    max_seqlen_kv = int((seqlens_kv.max().item() + 63) // 64 * 64)
6316
                batch_size = len(cu_seqlens_q) - 1
6317

6318
6319
6320
            cp_size = 1 if self.cp_group is None else get_distributed_world_size(self.cp_group)
            context_parallel = cp_size > 1

6321
            if qkv_format in ["sbhd", "bshd"]:
6322
                assert all(
6323
6324
6325
6326
                    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":
                    max_seqlen_q, max_seqlen_kv = (query_layer.shape[0], key_layer.shape[0])
6327
                    batch_size = query_layer.shape[1]
6328
6329
                if qkv_format == "bshd":
                    max_seqlen_q, max_seqlen_kv = (query_layer.shape[1], key_layer.shape[1])
6330
                    batch_size = query_layer.shape[0]
6331
6332
                max_seqlen_q *= cp_size
                max_seqlen_kv *= cp_size
6333
6334
6335
6336
6337
6338
6339
6340
6341
6342
6343
6344
                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
                        the sequence dimention in 'query_layer'!"""
                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
                        the sequence dimention in 'key_layer' and 'value_layer'!"""
6345
6346
6347
6348
6349
                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!"
6350
6351
                        if self.attention_type == "self":
                            assert max_seqlen_q == max_seqlen_kv
6352
6353
6354
6355
6356
6357
6358
6359
6360
6361
6362
6363
6364
6365
6366
6367
                            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,
                        )
6368

6369
6370
6371
6372
6373
            if (
                isinstance(query_layer, Float8Tensor)
                and isinstance(key_layer, Float8Tensor)
                and isinstance(value_layer, Float8Tensor)
            ):
6374
                qkv_layout, query_layer._data, key_layer._data, value_layer._data = get_qkv_layout(
6375
6376
6377
                    query_layer._data, key_layer._data, value_layer._data, qkv_format=qkv_format
                )
            else:
6378
                qkv_layout, query_layer, key_layer, value_layer = get_qkv_layout(
6379
6380
                    query_layer, key_layer, value_layer, qkv_format=qkv_format
                )
6381

6382
6383
6384
6385
6386
6387
6388
6389
            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
6390
            bottom_right_alignment = (attn_mask_type not in ["causal", "padding_causal"],)
6391
6392
6393
6394
6395
6396
6397
6398
            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
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                    or _alibi_cache["_bottom_right_alignment"] != bottom_right_alignment
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                    or _alibi_cache["_alibi_slopes"] is None
                ):
                    _alibi_cache["_alibi_slopes_require_update"] = True
                    _alibi_cache["_alibi_bias_require_update"] = True

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            core_attention_bias_shape = None
            if core_attention_bias is not None:
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                if (
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                    core_attention_bias.shape[0] == batch_size
                    and core_attention_bias.shape[1] == query_layer.shape[-2]
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                ):
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                    core_attention_bias_shape = "bhss"
                elif (
                    core_attention_bias.shape[0] == 1
                    and core_attention_bias.shape[1] == query_layer.shape[-2]
                ):
                    core_attention_bias_shape = "1hss"
                elif (
                    core_attention_bias.shape[0] == batch_size and core_attention_bias.shape[1] == 1
                ):
                    core_attention_bias_shape = "b1ss"
                elif core_attention_bias.shape[0] == 1 and core_attention_bias.shape[1] == 1:
                    core_attention_bias_shape = "11ss"
                else:
                    assert (
                        False
                    ), "core_attention_bias must be in one of {bhss, 1hss, b1ss, 11ss} shapes"

            pad_between_seqs = (
                cu_seqlens_q_padded is not None
                and not torch.equal(cu_seqlens_q_padded, cu_seqlens_q)
            ) or (
                cu_seqlens_kv_padded is not None
                and not torch.equal(cu_seqlens_kv_padded, cu_seqlens_kv)
            )
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            attention_params = AttentionParams(
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                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,
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                head_dim_qk=query_layer.shape[-1],
                head_dim_v=value_layer.shape[-1],
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                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,
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                deterministic=self.deterministic,
                is_training=self.training,
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                fp8=self.fp8,
                fp8_meta=self.fp8_meta,
            )
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            global _attention_backends
            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"]:
                (
                    use_flash_attention,
                    use_fused_attention,
                    fused_attention_backend,
                    use_unfused_attention,
                    _,
                ) = get_attention_backend(attention_params)
                if use_flash_attention:
                    self.logger.info("Running with FlashAttention backend")
                elif use_fused_attention:
                    self.logger.info(
                        "Running with FusedAttention backend (sub-backend %s)",
                        int(fused_attention_backend),
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                    )
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                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"]
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            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,
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                    cp_comm_type=self.cp_comm_type,
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                    max_seqlen_q=max_seqlen_q,
                    max_seqlen_kv=max_seqlen_kv,
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                )
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            if use_fused_attention:
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                fu_core_attention_bias_type = core_attention_bias_type
                fu_core_attention_bias = core_attention_bias
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                if core_attention_bias_type == "alibi" and (
                    alibi_slopes is not None or max_seqlen_q != max_seqlen_kv
                ):
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                    fu_core_attention_bias_type = "post_scale_bias"
                    _, fu_core_attention_bias = get_alibi(
                        query_layer.shape[-2],
                        max_seqlen_q,
                        max_seqlen_kv,
                        alibi_slopes=alibi_slopes,
                        bias_dtype=query_layer.dtype,
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                        bottom_right_alignment=attn_mask_type not in ["causal", "padding_causal"],
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                    )
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                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,
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                        cu_seqlens_q_padded=cu_seqlens_q_padded,
                        cu_seqlens_kv_padded=cu_seqlens_kv_padded,
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                        max_seqlen_q=max_seqlen_q,
                        max_seqlen_kv=max_seqlen_kv,
                        attn_mask_type=attn_mask_type,
                        attention_mask=attention_mask,
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                        window_size=window_size,
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                        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,
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                        cp_comm_type=self.cp_comm_type,
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                        fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                        fp8_meta=self.fp8_meta,
                    )
                return self.fused_attention(
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                    query_layer,
                    key_layer,
                    value_layer,
                    qkv_layout=qkv_layout,
                    cu_seqlens_q=cu_seqlens_q,
                    cu_seqlens_kv=cu_seqlens_kv,
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                    cu_seqlens_q_padded=cu_seqlens_q_padded,
                    cu_seqlens_kv_padded=cu_seqlens_kv_padded,
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                    max_seqlen_q=max_seqlen_q,
                    max_seqlen_kv=max_seqlen_kv,
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                    attn_mask_type=attn_mask_type,
                    attention_mask=attention_mask,
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                    window_size=window_size,
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                    fused_attention_backend=fused_attention_backend,
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                    core_attention_bias_type=fu_core_attention_bias_type,
                    core_attention_bias=fu_core_attention_bias,
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                    fast_zero_fill=fast_zero_fill,
                    cp_group=self.cp_group,
                    cp_global_ranks=self.cp_global_ranks,
                    cp_stream=self.cp_stream,
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                    cp_comm_type=self.cp_comm_type,
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                    fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                    fp8_meta=self.fp8_meta,
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                )
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            from .cpu_offload import CPUOffloadEnabled
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            if CPUOffloadEnabled:
                warnings.warn(
                    "Attention activation Offloading is only implemented"
                    "with Flash Attention and Fused Attention!"
                )
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            if use_unfused_attention:
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                if window_size is not None and (
                    window_size[0] != -1 or window_size[1] not in [-1, 0]
                ):
                    attn_mask_type, attention_mask = get_swa_mask(
                        window_size, max_seqlen_q, max_seqlen_kv, attn_mask_type, attention_mask
                    )
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                if checkpoint_core_attention:
                    return self._checkpointed_attention_forward(
                        self.unfused_attention,
                        query_layer,
                        key_layer,
                        value_layer,
                        qkv_layout=qkv_layout,
                        cu_seqlens_q=cu_seqlens_q,
                        cu_seqlens_kv=cu_seqlens_kv,
                        attn_mask_type=attn_mask_type,
                        attention_mask=attention_mask,
                        core_attention_bias_type=core_attention_bias_type,
                        core_attention_bias=core_attention_bias,
                        alibi_slopes=alibi_slopes,
                    )
                return self.unfused_attention(
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                    query_layer,
                    key_layer,
                    value_layer,
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                    qkv_layout=qkv_layout,
                    cu_seqlens_q=cu_seqlens_q,
                    cu_seqlens_kv=cu_seqlens_kv,
                    attn_mask_type=attn_mask_type,
                    attention_mask=attention_mask,
                    core_attention_bias_type=core_attention_bias_type,
                    core_attention_bias=core_attention_bias,
                    alibi_slopes=alibi_slopes,
                )
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            raise Exception("No dot product attention support for the provided inputs!")
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class MultiheadAttention(torch.nn.Module):
    r"""
    Multi-head Attention (MHA), including Query,
    Key, Value and Output projection.

    .. note::

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        Argument :attr:`attention_mask` in the `forward` call is only used when
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
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    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.
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    attn_mask_type: {'no_mask', 'padding', 'causal', 'padding_causal', 'causal_bottom_right',
                   'padding_causal_bottom_right','arbitrary'},
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                   default = `causal`
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                   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.
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    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
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                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
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                be overridden by :attr:`window_size` in `forward` as well.
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    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.
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    input_layernorm: bool, default = `False`
                     if set to `True`, layer normalization to the input is applied.
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    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"
          The device on which the parameters of the model will allocated. It is the user's
          responsibility to ensure all parameters are moved to the GPU before running the
          forward pass.
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    qkv_format: str, default = `sbhd`
            dimension format for `query_layer`, `key_layer` and `value_layer`,
            {`sbhd`, `bshd`}. `s` stands for the sequence length, `b` batch size,
            `h` the number of heads and `d` head size. `sbhd` and `bshd` formats
            are used for when sequences in a batch are of equal length or padded to
            equal length. Please note that these formats do not reflect how
            tensors `query_layer`, `key_layer`, `value_layer` are laid out in memory.
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            For that, please use `get_qkv_layout` to gain the layout information.
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    Parallelism parameters
    ----------------------
    set_parallel_mode : bool, default = `False`
                      if set to `True`, QKV and FC1 layers are used as Column Parallel
                      whereas PROJ and FC2 is used as Row Parallel as described
                      `here <https://arxiv.org/pdf/1909.08053.pdf>`_.
    sequence_parallel : bool, default = `False`
                       if set to `True`, uses sequence parallelism.
    tp_group : ProcessGroup, default = `None`
              tensor parallel process group.
    tp_size : int, default = 1
             used as TP (tensor parallel) world size when TP groups are not formed during
             initialization. In this case, users must call the
             `set_tensor_parallel_group(tp_group)` method on the initialized module before the
             forward pass to supply the tensor parallel group needed for tensor and sequence
             parallel collectives.

    Optimization parameters
    -----------------------
    fuse_wgrad_accumulation : bool, default = 'False'
                             if set to `True`, enables fusing of creation and accumulation of
                             the weight gradient. When enabled, it is assumed that the weights
                             have an additional `main_grad` attribute (used instead of the
                             regular `grad`) which is a pre-allocated buffer of the correct
                             size to accumulate gradients in.
    params_dtype : torch.dtype, default = `torch.get_default_dtype()`
                  it controls the type used to allocate the initial parameters. Useful when
                  the model is trained with lower precision and the original FP32 parameters
                  would not fit in GPU memory.
    return_bias : bool, default = `False`
                 when set to `True`, this module will not apply the additive bias itself, but
                 instead return the bias value during the forward pass together with the
                 output of the linear transformation :math:`y = xA^T`. This is useful when
                 the bias addition can be fused to subsequent operations.
    fuse_qkv_params: bool, default = 'False'
                    if set to `True`, `TransformerLayer` module exposes a single fused
                    parameter for query-key-value. This enables optimizations such as QKV
                    fusion without concatentations/splits and also enables the argument
                    `fuse_wgrad_accumulation`.
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    """

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

        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:
        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,
        cp_group: Union[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
        ----------
        cp_group : ProcessGroup
                  context parallel process group.
        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
                      inter-gpu communication type for context parallelism.
                      Can be "p2p" or "all_gather".
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        """
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        # Deep iterate but skip self to avoid infinite recursion.
        for index, child in enumerate(self.modules()):
            if index == 0:
                continue
            if hasattr(child, "set_context_parallel_group"):
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                child.set_context_parallel_group(cp_group, cp_global_ranks, cp_stream, cp_comm_type)
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    def forward(
        self,
        hidden_states: torch.Tensor,
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        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
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        encoder_output: Optional[torch.Tensor] = None,
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        attn_mask_type: Optional[str] = None,
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        window_size: Optional[Tuple[int, int]] = None,
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        is_first_microbatch: Optional[bool] = None,
        checkpoint_core_attention: bool = False,
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        inference_params: Optional[InferenceParams] = None,
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        rotary_pos_emb: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
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        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
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        alibi_slopes: Optional[torch.Tensor] = None,
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        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
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        fast_zero_fill: bool = True,
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    ) -> Tuple[Union[torch.Tensor, None], ...]:
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        """
        Forward propagation for MultiheadAttention layer.

        .. note::

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            Argument :attr:`attention_mask` is only used when :attr:`attn_mask_type`
            includes `"padding"` or `"arbitrary"`.
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        Parameters
        ----------
        hidden_states : torch.Tensor
             Input tensor.
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        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]],
             default = `None`. Boolean tensor(s) used to mask out attention softmax input.
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             It should be `None` for causal masks and "`no_mask`". For padding masks, it should be
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             a single tensor of [batch_size, 1, 1, seqlen_q] for self-attention, and a tuple of
             two tensors in shapes [batch_size, 1, 1, seqlen_q] and [batch_size, 1, 1, seqlen_kv]
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             for cross-attention. For "`arbitrary`" mask, it should be in a shape broadcastable to
             [batch_size, num_heads, max_seqlen_q, max_seqlen_kv]. A `True` value means
             the corresponding position is masked out and a `False` means that position
             is allowed to participate in attention.
        attn_mask_type: {'no_mask', 'padding', 'causal', 'padding_causal', 'causal_bottom_right',
                       'padding_causal_bottom_right','arbitrary'},
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                       default = `None`
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                       type of attention mask passed into softmax operation. By default,
                       causal masks are aligned to the top left corner of the softmax matrix.
                       When "`bottom_right`" is specified in the mask type, causal masks are
                       aligned to the bottom right corner.
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        window_size: Optional[Tuple[int, int]], default = `None`
                    sliding window size for local attention.
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        encoder_output : Optional[torch.Tensor], default = `None`
             Output of the encoder block to be fed into the decoder block if using
             `layer_type="decoder"`.
        is_first_microbatch : {True, False, None}, default = None
                             During training using either gradient accumulation or
                             pipeline parallelism a minibatch of data is further split
                             into microbatches. Between the microbatches of the same minibatch
                             the model weights are not updated. Setting this parameter indicates
                             whether the current microbatch is the first in a minibatch or not.
                             When set, this parameter enables additional optimizations:

                             * during FP8 training, it allows caching of the FP8 versions of
                               the weights
                             * it also allows skipping gradient accumulation during the
                               first microbatch (since it is the first gradient being
                               produced)
        checkpoint_core_attention: bool, default = `False`
                                  If true, forward activations for core attention are recomputed
                                  during the backward pass in order to save memory that would
                                  otherwise be occupied to store the forward activations until
                                  backprop.
        rotary_pos_emb: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]], default = `None`
                       Embeddings for query and key tensors for applying rotary position
                       embedding. By default no input embedding is applied.
        core_attention_bias_type: str, default = `no_bias`
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                    Bias type, {`no_bias`, `pre_scale_bias`, 'post_scale_bias`, `alibi`}
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        core_attention_bias: Optional[torch.Tensor], default = `None`
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                    Bias tensor for Q * K.T, shape [1, num_head, max_seqlen_q, max_seqlen_kv].
                    It should be 'None' for 'no_bias' and 'alibi' bias types.
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        alibi_slopes: Optional[torch.Tensor], default = `None`
                     ALiBi slopes in FP32 and shape [nheads] or [batch_size, nheads].
                     It adds a bias of (-alibi_slope * (i + seqlen_k - seqlen_q - j))
                     to the attention score of query i and key j.
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        cu_seqlens_q: Optional[torch.Tensor], default = `None`
                   Cumulative sum of sequence lengths (without offset) in a batch for `query_layer`,
                   with shape [batch_size + 1] and dtype torch.int32.
        cu_seqlens_kv: Optional[torch.Tensor], default = `None`
                   Cumulative sum of sequence lengths (without offset) in a batch for `key_layer`
                   and `value_layer`, with shape [batch_size + 1] and dtype torch.int32.
        max_seqlen_q: Optional[int], default = `None`
                      Maximum sequence length in `query_layer`.
                      Calculated from `cu_seqlens_q` if not provided.
        max_seqlen_kv: Optional[int], default = `None`
                       Maximum sequence length in `key_layer` and `value_layer`.
                       Calculated from `cu_seqlens_kv` if not provided.
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        fast_zero_fill: bool, default = `True`
                    Whether to set output tensors to 0 or not before use.
        """
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        # hidden_states: [sq, b, h]

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        if attn_mask_type is None:
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            attn_mask_type = self.attn_mask_type
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        if window_size is None:
            window_size = self.window_size
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        window_size = check_set_window_size(attn_mask_type, window_size)
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        if "padding" in attn_mask_type and attention_mask is not None:
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            for i, _ in enumerate(attention_mask):
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                assert (
                    attention_mask[i].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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        # ======================
7181
        # Query, Key, and Value
7182
        # ======================
7183

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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,
                )
                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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                    is_first_module_in_mha=True,  # specific to FP8 MHA
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                )

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

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            # qkv_weight_interleaved:
            #  [sq, b, ng, (np/ng + 2), hn]
            #  --> [sq, b, ng, np/ng, hn], [sq, b, ng, 1, hn], [sq, b, ng, 1, hn]
            # not qkv_weight_interleaved:
            #  [sq, b, (np/ng + 2), ng, hn]
            #  --> [sq, b, np/ng, np, hn], [sq, b, 1, ng, hn], [sq, b, 1, ng, hn]
            if not is_in_onnx_export_mode():
                query_layer, key_layer, value_layer = _SplitAlongDim.apply(
                    mixed_x_layer, split_dim, (num_queries_per_key_value, 1, 1)
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                )
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            else:
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                query_layer, key_layer, value_layer = torch.split(
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                    mixed_x_layer,
                    (num_queries_per_key_value, 1, 1),
                    dim=split_dim,
                )
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            if self.qkv_format == "thd":
                query_layer, key_layer, value_layer = (
                    x.reshape(x.size(0), -1, self.hidden_size_per_attention_head)
                    for x in (query_layer, key_layer, value_layer)
                )
            else:
                # query: -> [sq, b, np, hn]
                # key, value: -> [sq, b, ng, hn]
                query_layer, key_layer, value_layer = (
                    x.reshape(x.size(0), x.size(1), -1, self.hidden_size_per_attention_head)
                    for x in (query_layer, key_layer, value_layer)
                )
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        elif self.attention_type == "cross":
            # Attention heads [sk, b, h] --> [sk, b, (ng * 2 * hn)]
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            mixed_kv_layer = self.key_value(
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                encoder_output,
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                is_first_microbatch=is_first_microbatch,
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                is_first_module_in_mha=True,  # specific to FP8 MHA
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            )

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

            mixed_kv_layer = mixed_kv_layer.view(*new_tensor_shape)

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            # mixed_kv_layer --> 2 [sk, b, ng, hn]
            if not is_in_onnx_export_mode():
                key_layer, value_layer = _SplitAlongDim.apply(
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                    mixed_kv_layer,
                    split_dim,
                    mixed_kv_layer.shape[split_dim] // 2,
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                )
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            else:
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                key_layer, value_layer = torch.split(
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                    mixed_kv_layer,
                    mixed_kv_layer.shape[split_dim] // 2,
                    dim=split_dim,
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                )
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            key_layer, value_layer = (
                x.reshape(
                    x.size(0),
                    x.size(1),
                    -1,
                    self.hidden_size_per_attention_head,
                )
                for x in (key_layer, value_layer)
            )
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            # Attention head [sq, b, h] --> [sq, b, hp]
            if self.input_layernorm:
                layernorm_query_outputs = self.layernorm_query(
                    hidden_states,
                    is_first_microbatch=is_first_microbatch,
                )
                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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                    is_first_module_in_mha=True,  # specific to FP8 MHA
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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)

                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)
            key_layer = apply_rotary_pos_emb(key_layer, k_pos_emb, self.qkv_format, fused=True)
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        # ===========================
        # Core attention computation
        # ===========================

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

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

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

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