attention.py 326 KB
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# Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
# See LICENSE for license information.

"""Attention."""
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import collections
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from contextlib import nullcontext
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from importlib.metadata import version as get_pkg_version
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from importlib.metadata import PackageNotFoundError
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import math
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import os
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from typing import Any, Callable, Dict, List, Optional, Tuple, Union
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import warnings
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import logging
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import 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 transformer_engine_torch as tex
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from transformer_engine.pytorch.utils import (
    get_cudnn_version,
    nvtx_range_pop,
    nvtx_range_push,
)
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from transformer_engine.pytorch.cpp_extensions.fused_attn import (
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    fused_attn_fwd,
    fused_attn_bwd,
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    FusedAttnBackend,
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    META_QKV,
    META_O,
)
from transformer_engine.pytorch.fp8 import (
    FP8GlobalStateManager,
    get_fp8_te_dtype,
    get_fp8_torch_dtype,
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)
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from transformer_engine.pytorch.float8_tensor import Float8Tensor
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from transformer_engine.pytorch.tensor._internal.float8_tensor_base import Float8TensorBase
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from transformer_engine.pytorch.module import LayerNormLinear, Linear
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from transformer_engine.pytorch.module.base import TransformerEngineBaseModule
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from transformer_engine.pytorch.utils import (
    divide,
    attention_mask_func,
    split_tensor_along_dim,
    get_device_compute_capability,
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    get_default_init_method,
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)
from transformer_engine.pytorch.constants import (
    AttnMaskTypes,
    AttnTypes,
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    AttnBiasTypes,
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    QKVLayouts,
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    dist_group_type,
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    TE_DType,
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)
from transformer_engine.pytorch.softmax import FusedScaleMaskSoftmax
from transformer_engine.pytorch.distributed import (
    get_distributed_world_size,
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    get_distributed_rank,
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    checkpoint,
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    set_all_rng_states,
    CudaRNGStatesTracker,
    graph_safe_rng_available,
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    gather_along_first_dim,
    reduce_scatter_along_first_dim,
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)
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from transformer_engine.pytorch.jit import jit_fuser, no_torch_dynamo
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from transformer_engine.pytorch.graph import is_graph_capturing
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from transformer_engine.pytorch.dot_product_attention.inference import InferenceParams
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from transformer_engine.pytorch.tensor.quantized_tensor import (
    QuantizedTensor,
    prepare_for_saving,
    restore_from_saved,
)
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# Import attention utils
import transformer_engine.pytorch.dot_product_attention.utils as dpa_utils
from transformer_engine.pytorch.dot_product_attention.utils import FlashAttentionUtils as fa_utils
from transformer_engine.pytorch.dot_product_attention.utils import AttentionLogging as attn_log
from transformer_engine.pytorch.dot_product_attention.rope import apply_rotary_pos_emb
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# Setup Attention Logging
attn_log.setup_logging()

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# Global vars for flash attn v2 and v3 imports
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flash_attn_cuda_bwd = None
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flash_attn_func = None
flash_attn_varlen_func = None
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_flash_attn_fwd = None
_flash_attn_bwd = None
_flash_attn_varlen_fwd = None
_flash_attn_varlen_bwd = None
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try:
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    fa_utils.version = PkgVersion(get_pkg_version("flash-attn"))
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except PackageNotFoundError:
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    pass  # only print warning if use_flash_attention_2 = True in get_attention_backend
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else:
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    if torch.cuda.is_available() and get_device_compute_capability() >= (10, 0):
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        if fa_utils.version_required_blackwell <= fa_utils.version <= fa_utils.max_version:
            fa_utils.is_installed = True
    elif fa_utils.version_required <= fa_utils.version <= fa_utils.max_version:
        fa_utils.is_installed = True
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    if fa_utils.is_installed:
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        from flash_attn_2_cuda import varlen_bwd as flash_attn_cuda_bwd
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        from flash_attn.flash_attn_interface import flash_attn_func, flash_attn_varlen_func
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        from flash_attn.flash_attn_interface import _flash_attn_forward as _flash_attn_fwd
        from flash_attn.flash_attn_interface import _flash_attn_backward as _flash_attn_bwd
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        from flash_attn.flash_attn_interface import (
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            _flash_attn_varlen_forward as _flash_attn_varlen_fwd,
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        )
        from flash_attn.flash_attn_interface import (
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            _flash_attn_varlen_backward as _flash_attn_varlen_bwd,
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        )

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        # Setup Flash attention utils
        fa_utils.set_flash_attention_version()
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    elif (
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        torch.cuda.is_available()
        and get_device_compute_capability() >= (8, 0)
        and dpa_utils._NVTE_FLASH_ATTN
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    ):
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        attn_log.fa_logger.warning(
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            "Supported flash-attn versions are %s. Found flash-attn %s.",
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            dpa_utils._get_supported_versions(
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                (
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                    fa_utils.version_required
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                    if get_device_compute_capability() < (10, 0)
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                    else fa_utils.version_required_blackwell
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                ),
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                fa_utils.max_version,
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            ),
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            fa_utils.version,
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        )
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try:
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    fa_utils.fa3_version = PkgVersion(get_pkg_version("flash-attn-3"))
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except PackageNotFoundError:
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    pass  # only print warning if use_flash_attention_3 = True in get_attention_backend
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else:
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    from flash_attn_3.flash_attn_interface import flash_attn_func as flash_attn_func_v3
    from flash_attn_3.flash_attn_interface import (
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        flash_attn_varlen_func as flash_attn_varlen_func_v3,
    )
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    from flash_attn_3.flash_attn_interface import (
        flash_attn_with_kvcache as flash_attn_with_kvcache_v3,
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    )
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    from flash_attn_3.flash_attn_interface import _flash_attn_forward as _flash_attn_fwd_v3
    from flash_attn_3.flash_attn_interface import _flash_attn_backward as _flash_attn_bwd_v3
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    fa_utils.set_flash_attention_3_params()
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# Global vars for available attention backends and ALiBi cache
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_attention_backends = {
    "attention_params": None,
    "use_flash_attention": None,
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    "flash_attention_backend": None,
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    "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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_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,
}

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


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def flash_attn_p2p_communicate(
    rank, send_tensor, send_dst, recv_tensor, recv_src, cp_group, batch_p2p_comm
):
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    """Point-to-point communications of KV and dKV in Attention with context parallelism"""
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    send_recv_ops = []

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

    return send_recv_reqs


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@jit_fuser
def flash_attn_fwd_out_correction_init(
    out_init_step: torch.Tensor,
    softmax_lse: torch.Tensor,
    softmax_lse_init_step: torch.Tensor,
    seq_dim: int,
):
    """Merge partial outputs of the first step in Attention with context parallelism"""
    softmax_lse_corrected_exp = torch.exp(softmax_lse_init_step - softmax_lse).movedim(2, seq_dim)
    softmax_lse_corrected_exp = softmax_lse_corrected_exp.unsqueeze(-1)
    out_corrected = out_init_step * softmax_lse_corrected_exp
    return out_corrected.to(out_init_step.dtype)


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@jit_fuser
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def flash_attn_fwd_out_correction(
    out: torch.Tensor,
    out_per_step: torch.Tensor,
    softmax_lse: torch.Tensor,
    softmax_lse_per_step: torch.Tensor,
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    seq_dim: int,
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):
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    """Merge partial outputs of each step in Attention with context parallelism"""
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    softmax_lse_corrected_exp = torch.exp(softmax_lse_per_step - softmax_lse).movedim(2, seq_dim)
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    softmax_lse_corrected_exp = softmax_lse_corrected_exp.unsqueeze(-1)
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    out_corrected = out_per_step * softmax_lse_corrected_exp
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    out.add_(out_corrected)


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@jit_fuser
def flash_attn_fwd_second_half_out_correction(
    out: torch.Tensor,
    out_per_step: torch.Tensor,
    softmax_lse: torch.Tensor,
    softmax_lse_per_step: torch.Tensor,
    seq_dim: int,
):
    """Merge second half of partial outputs of each step in Attention with context parallelism"""
    out_ = out.select(seq_dim, 1)
    softmax_lse_ = softmax_lse.view(*softmax_lse.shape[:-1], 2, -1)[..., 1, :]
    softmax_lse_corrected_exp = torch.exp(softmax_lse_per_step - softmax_lse_).movedim(2, seq_dim)
    softmax_lse_corrected_exp = softmax_lse_corrected_exp.unsqueeze(-1)
    out_corrected = out_per_step * softmax_lse_corrected_exp
    out_.add_(out_corrected)


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@jit_fuser
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def flash_attn_fwd_softmax_lse_correction(
    softmax_lse: torch.Tensor,
    softmax_lse_per_step: torch.Tensor,
):
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    """Merge softmax stats of each step in Attention with context parallelism"""
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    max_scale = torch.max(softmax_lse, softmax_lse_per_step)
    min_scale = torch.min(softmax_lse, softmax_lse_per_step)
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    new_scale = max_scale + torch.log1p(torch.exp(min_scale - max_scale))
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    softmax_lse.copy_(new_scale)
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@jit_fuser
def flash_attn_fwd_second_half_softmax_lse_correction(
    softmax_lse: torch.Tensor,
    softmax_lse_per_step: torch.Tensor,
):
    """Merge second half of softmax stats of each step in Attention with context parallelism"""
    softmax_lse_ = softmax_lse[..., 1, :]
    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.log1p(torch.exp(min_scale - max_scale))
    softmax_lse_.copy_(new_scale)


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@jit_fuser
def get_cu_seqlens_on_cp_rank(
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    cu_seqlens: torch.Tensor,
    cu_seqlens_padded_on_cp_rank: torch.Tensor,
    cp_size: int,
    cp_rank: int,
    first_half: bool,
    second_half: bool,
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):
    """Compute cu_seqlens of a context parallelism rank"""
    seqlens = cu_seqlens[1:] - cu_seqlens[:-1]
    seqlens_padded = (cu_seqlens_padded_on_cp_rank[1:] - cu_seqlens_padded_on_cp_rank[:-1]) // 2
    zeros = torch.zeros_like(seqlens)
    cu_seqlens_on_cp_rank = torch.zeros_like(cu_seqlens)
    if first_half:
        seqlens_1 = seqlens - cp_rank * seqlens_padded
        seqlens_1 = seqlens_1.clamp(zeros, seqlens_padded)
        cu_seqlens_on_cp_rank[1:].add_(seqlens_1)
    if second_half:
        seqlens_2 = seqlens - (2 * cp_size - cp_rank - 1) * seqlens_padded
        seqlens_2 = seqlens_2.clamp(zeros, seqlens_padded)
        cu_seqlens_on_cp_rank[1:].add_(seqlens_2)
    cu_seqlens_on_cp_rank.cumsum_(dim=0)
    return cu_seqlens_on_cp_rank


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@jit_fuser
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def get_seq_chunk_ids_for_reordering_before_attn(cp_size, device):
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    """
    Context parallelism assigns two discontiguous sequence chunks to each GPU for load balancing.
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    To make sure tokens are ordered correctly for compute, we need to reorder sequence chunks to
    be contigupus before attention compute. This function is to compute sequence chunk ids for
    reordering.
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    """
    chunk_ids = torch.empty(2 * cp_size, dtype=torch.int32, device=device)
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    for rank in range(cp_size):
        chunk_ids[rank] = 2 * rank
        chunk_ids[rank + cp_size] = 2 * cp_size - 2 * rank - 1
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    return chunk_ids


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@jit_fuser
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def get_seq_chunk_ids_for_reordering_after_attn(cp_size, device):
    """
    Context parallelism assigns two discontiguous sequence chunks to each GPU for load balancing.
    We need to reorder sequence chunks back to discontiguous after attention compute. This function
    is to compute sequence chunk ids for reordering.
    """
    chunk_ids = torch.empty(2 * cp_size, dtype=torch.int32, device=device)
    for rank in range(cp_size):
        chunk_ids[2 * rank] = rank
        chunk_ids[2 * rank + 1] = 2 * cp_size - rank - 1
    return chunk_ids


@jit_fuser
def reorder_seq_chunks_for_a2a_before_attn(x, chunk_ids_for_a2a, seq_dim, cp_size):
    """Reorder sequence chunk for A2A communication before attention compute."""
    # [cp, b, s, np//cp, hn] -> [b, cp, s, np//cp, hn]
    # or [cp, s, b, np//cp, hn] -> [cp, s, b, np//cp, hn]
    x = x.movedim(0, seq_dim).contiguous()
    # [b, cp, s, np//cp, hn] -> [b, cp*2, s//2, np//cp, hn]
    # or [cp, s, b, np//cp, hn] -> [cp*2, s//2, b, np//cp, hn]
    x = x.view(*x.shape[:seq_dim], cp_size * 2, -1, *x.shape[(seq_dim + 2) :])
    # reorder the sequence chunks
    x = torch.index_select(x, dim=seq_dim, index=chunk_ids_for_a2a)
    return x


@jit_fuser
def reorder_seq_chunks_for_a2a_after_attn(x, chunk_ids_for_a2a, seq_dim, cp_size):
    """Reorder sequence chunk for A2A communication after attention compute."""
    # [b, cp*2, s//2, np//cp, hn] -> [cp*2, b, s//2, np//cp, hn]
    # or [cp*2, s//2, b, np//cp, hn] -> [cp*2, s//2, b, np//cp, hn]
    x = x.movedim(seq_dim, 0).contiguous()
    # reorder the sequence chunks
    x = torch.index_select(x, dim=0, index=chunk_ids_for_a2a)
    # [cp*2, b, s//2, np//cp, hn] -> [cp, 2, b, s//2, np//cp, hn]
    # or [cp*2, s//2, b, np//cp, hn] -> [cp, 2, s//2, b, np//cp, hn]
    x = x.view(cp_size, 2, *x.shape[1:])
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    return x


def flash_attn_a2a_communicate(
    a2a_inputs: Union[torch.Tensor, List[torch.Tensor]],
    chunk_ids_for_a2a: torch.Tensor,
    seq_dim: int,
    cp_size: int,
    cp_group: dist_group_type,
    cp_stream: torch.cuda.Stream,
    before_attn: bool,
) -> Union[torch.Tensor, List[torch.Tensor]]:
    """A2A communication for context parallelism."""
    a2a_inputs = [a2a_inputs] if not isinstance(a2a_inputs, list) else a2a_inputs
    a2a_outputs, a2a_reqs = [None] * len(a2a_inputs), [None] * len(a2a_inputs)
    if before_attn:
        for i in range(len(a2a_inputs) + 2):
            if 0 < i < len(a2a_inputs) + 1:
                a2a_outputs[i - 1] = torch.empty_like(a2a_inputs[i - 1])
                a2a_reqs[i - 1] = torch.distributed.all_to_all_single(
                    a2a_outputs[i - 1], a2a_inputs[i - 1], group=cp_group, async_op=True
                )
            if i > 1:
                with torch.cuda.stream(cp_stream):
                    a2a_reqs[i - 2].wait()
                    x = a2a_outputs[i - 2]
                    # reorder the sequence chunks
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                    x = reorder_seq_chunks_for_a2a_before_attn(
                        x, chunk_ids_for_a2a, seq_dim, cp_size
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                    )
                    # [b, cp*2, s//2, np//cp, hn] -> [b, cp*s, np//cp, hn]
                    # or [cp*2, s//2, b, np//cp, hn] -> [cp*s, b, np//cp, hn]
                    a2a_outputs[i - 2] = x.view(*x.shape[:seq_dim], -1, *x.shape[(seq_dim + 2) :])
            if i < len(a2a_inputs):
                x = a2a_inputs[i]
                # [b, s, np, hn] -> [b, s, cp, np//cp, hn]
                # or [s, b, np, hn] -> [s, b, cp, np//cp, hn]
                x = x.view(*x.shape[:-2], cp_size, x.shape[-2] // cp_size, x.shape[-1])
                # [b, s, cp, np//cp, hn] -> [cp, b, s, np//cp, hn]
                # or [s, b, cp, np//cp, hn] -> [cp, s, b, np//cp, hn]
                a2a_inputs[i] = x.movedim(-3, 0).contiguous()
    else:
        for i in range(len(a2a_inputs) + 2):
            if 0 < i < len(a2a_inputs) + 1:
                a2a_outputs[i - 1] = torch.empty_like(a2a_inputs[i - 1])
                a2a_reqs[i - 1] = torch.distributed.all_to_all_single(
                    a2a_outputs[i - 1], a2a_inputs[i - 1], group=cp_group, async_op=True
                )
            if i < len(a2a_inputs):
                x = a2a_inputs[i]
                # [b, cp*s, np//cp, hn] -> [b, cp*2, s//2, np//cp, hn]
                # or [cp*s, b, np//cp, hn] -> [cp*2, s//2, b, np//cp, hn]
                x = x.view(*x.shape[:seq_dim], cp_size * 2, -1, *x.shape[(seq_dim + 1) :])
                # reorder the sequence chunks
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                a2a_inputs[i] = reorder_seq_chunks_for_a2a_after_attn(
                    x, chunk_ids_for_a2a, seq_dim, cp_size
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                )
            if i > 1:
                with torch.cuda.stream(cp_stream):
                    a2a_reqs[i - 2].wait()
                    x = a2a_outputs[i - 2]
                    # [cp, 2, b, s//2, np//cp, hn] -> [b, 2, s//2, cp, np//cp, hn]
                    # or [cp, 2, s//2, b, np//cp, hn] -> [2, s//2, b, cp, np//cp, hn]
                    x = x.movedim(0, -3).movedim(0, seq_dim).contiguous()
                    # [b, 2, s//2, cp, np//cp, hn] -> [b*s, np, hn]
                    # or [2, s//2, b, cp, np//cp, hn] -> [s*b, np, hn]
                    a2a_outputs[i - 2] = x.view(-1, x.shape[-3] * x.shape[-2], x.shape[-1])
    torch.cuda.current_stream().wait_stream(cp_stream)
    return a2a_outputs[0] if len(a2a_inputs) == 1 else a2a_outputs


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_cu_seqlens_info_with_cp_cache = {}


def _get_cu_seqlens_info_with_cp(
    batch_size: int,
    max_seqlen: int,
    cp_size: int,
    cu_seqlens: torch.Tensor,
):
    """Cumulative sequence lengths with CP being considered."""
    global _cu_seqlens_info_with_cp_cache
    if (batch_size, max_seqlen, cp_size) not in _cu_seqlens_info_with_cp_cache:
        _cu_seqlens_info_with_cp_cache[(batch_size, max_seqlen, cp_size)] = (
            cu_seqlens // cp_size,
            cu_seqlens // (cp_size * 2),
        )
    return _cu_seqlens_info_with_cp_cache[(batch_size, max_seqlen, cp_size)]


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def get_fa_args(
    forward: bool,
    use_flash_attn_3: bool,
    qkv_format: str,
    cu_seqlens_q=None,
    cu_seqlens_kv=None,
    max_seqlen_q=None,
    max_seqlen_kv=None,
    dq=None,
    dk=None,
    dv=None,
):
    """Get forward/backward arguments for flash-attn v2 and v3."""
    if use_flash_attn_3:
        if forward:
            if qkv_format == "thd":
                return [
                    *[None] * 4,  # k_new, v_new, qv, out
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    *[None] * 3,  # cu_seqlens_k_new, seqused_q, seqused_k
                    max_seqlen_q,
                    max_seqlen_kv,
                    *[None]
                    * 8,  # page_table, kv_batch_idx, leftpad_k, rotary_cos, rotary_sin, q_descale, k_descale, v_descale
                ]
            return [
                *[None]
                * 9,  # k_new, v_new, qv, out, cu_seqlens_q, cu_seqlens_kv, cu_seqlens_k_new, seqused_q, seqused_k
                max_seqlen_q,
                max_seqlen_kv,
                *[None]
                * 8,  # page_table, kv_batch_idx, leftpad_k, rotary_cos, rotary_sin, q_descale, k_descale, v_descale
            ]
        if qkv_format == "thd":
            return [
                cu_seqlens_q,
                cu_seqlens_kv,
                None,  # sequed_q
                None,  # sequed_k
                max_seqlen_q,
                max_seqlen_kv,
                dq,
                dk,
                dv,
            ]
        return [
            None,  # cu_seqlens_q
            None,  # cu_seqlens_kv
            None,  # sequed_q
            None,  # sequed_k
            max_seqlen_q,
            max_seqlen_kv,
            dq,
            dk,
            dv,
        ]
    if forward:
        if qkv_format == "thd":
            return [
                cu_seqlens_q,
                cu_seqlens_kv,
                max_seqlen_q,
                max_seqlen_kv,
            ]
        return []
    if qkv_format == "thd":
        return [
            dq,
            dk,
            dv,
            cu_seqlens_q,
            cu_seqlens_kv,
            max_seqlen_q,
            max_seqlen_kv,
        ]
    return [
        dq,
        dk,
        dv,
    ]


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class AttnFuncWithCPAndKVP2P(torch.autograd.Function):
552
    """
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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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    This implementation also supports hierarchical CP, which parallelizes attention
    heads in low-level CP groups and parallelizes sequence dimension in high-level CP
    groups. For more details, please refer to `LongVILA <https://arxiv.org/abs/2408.10188>`_
    and `USP <https://arxiv.org/abs/2405.07719>`_.
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    """

    @staticmethod
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    def forward(
        ctx,
        is_training,
        q,
        k,
        v,
        cu_seqlens_q,
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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,
        softmax_scale,
        qkv_format,
        attn_mask_type,
        attn_bias_type,
        attn_bias,
        deterministic,
        use_fused_attention,
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        fp8,
        fp8_meta,
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        cp_group,
        cp_global_ranks,
        cp_stream,
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        quantizers,
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        pad_between_seqs,
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        use_flash_attn_3,
592
    ):
593
        # pylint: disable=missing-function-docstring
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        nvtx_range_push("transformer_engine.AttnFuncWithCPAndKVP2P.forward")
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        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)

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

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

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        causal = "causal" in attn_mask_type
        padding = "padding" in attn_mask_type
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        batch_dim = None
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        seq_dim = None
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        cu_seqlens_q_half, cu_seqlens_kv_half = None, None
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        if qkv_format in ["bshd", "sbhd"]:
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            seq_dim = qkv_format.index("s")
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            qkv_layout = qkv_format + "_" + qkv_format[:-2] + "2" + qkv_format[-2:]
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            cu_seqlens_q_padded, cu_seqlens_kv_padded = None, None
            if use_fused_attention:
                batch_dim = qkv_format.index("b")
                cu_seqlens_q, cu_seqlens_q_half = _get_cu_seqlens_info_with_cp(
                    q.shape[batch_dim], max_seqlen_q, cp_size, cu_seqlens_q
                )
                cu_seqlens_kv, cu_seqlens_kv_half = _get_cu_seqlens_info_with_cp(
                    q.shape[batch_dim], max_seqlen_kv, cp_size, cu_seqlens_kv
                )
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        else:
            qkv_layout = qkv_format + "_" + qkv_format + "_" + qkv_format
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            cu_seqlens_q_padded = cu_seqlens_q_padded // cp_size
            cu_seqlens_kv_padded = cu_seqlens_kv_padded // cp_size
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        max_seqlen_q = max_seqlen_q // cp_size
        max_seqlen_kv = max_seqlen_kv // cp_size
        cu_seqlens_q_per_step = [None for _ in range(cp_size)]
        cu_seqlens_kv_per_step = [None for _ in range(cp_size)]
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        fused_attn_backend = None
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        qkv_dtype = q.dtype
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        amax_per_step = None
        S_quantizer_per_step = [None for _ in range(cp_size)]
        O_CP_quantizer_per_step = [None for _ in range(cp_size)]
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        # "fp8_mha" decides outputs in fp8, while inputs are inferred from the real dtype
        is_input_fp8 = False
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        is_output_fp8 = False

        (
            QKV_quantizer,
            O_quantizer,
            O_CP_quantizer,
            S_quantizer,
            dQKV_quantizer,
            dQKV_CP_quantizer,
            dO_quantizer,
            dP_quantizer,
667
        ) = dpa_utils.get_attention_quantizers(fp8, quantizers, cp_specific_quantizers=True)
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        if fp8:
            if use_fused_attention:
                fused_attn_backend = FusedAttnBackend["FP8"]
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                assert isinstance(k, q.__class__) and isinstance(
                    v, q.__class__
                ), "q, k, and v must have the same type."
                is_input_fp8 = isinstance(q, Float8Tensor)
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                is_output_fp8 = fp8_meta is not None and fp8_meta["recipe"].fp8_mha
                if is_input_fp8:
                    QKV_quantizer = q._quantizer
                    q, k, v = q._data, k._data, v._data
                else:
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                    q_f16, k_f16, v_f16 = q, k, v
                    if cp_size_a2a == 1 or int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
684
                        q = QKV_quantizer(q_f16)._data
685
                    if int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
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                        k, v = [QKV_quantizer(x)._data for x in [k_f16, v_f16]]
                amax_per_step = torch.zeros((2, cp_size), dtype=torch.float32, device=q.device)
                # partial result quantizer
                for i in range(cp_size):
                    S_quantizer_per_step[i] = S_quantizer.copy()
691
                    S_quantizer_per_step[i].amax = amax_per_step[0][i].reshape((1,))
692
                    O_CP_quantizer_per_step[i] = O_CP_quantizer.copy()
693
                    O_CP_quantizer_per_step[i].amax = amax_per_step[1][i].reshape((1,))
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            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
            q_f16 = q
            if use_fused_attention:
                fused_attn_backend = FusedAttnBackend["F16_arbitrary_seqlen"]

        if cp_size_a2a > 1:
702
            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_before_attn(cp_size_a2a, q.device)
703

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            q, k, v = flash_attn_a2a_communicate(
                [q, k, v], chunk_ids_for_a2a, seq_dim, cp_size_a2a, cp_group_a2a, cp_stream, True
            )
            if not fp8:
                q_f16 = q
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            elif not is_input_fp8 and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
710
                q_f16 = q
711
                q = QKV_quantizer(q_f16)._data
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        assert qkv_format == "thd" or (
            q.shape[seq_dim] % 2 == 0 and k.shape[seq_dim] % 2 == 0
        ), "Sequence length per GPU needs to be divisible by 2!"
716
        if causal:
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            if qkv_format == "bshd":
                # [b, s, np, hn] -> [b, 2, s//2, np, hn]
719
                q, k, v = [x.view(x.shape[0], 2, x.shape[1] // 2, *x.shape[2:]) for x in [q, k, v]]
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            elif qkv_format == "sbhd":
                # [s, b, np, hn] -> [2, s//2, b, np, hn]
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                q, k, v = [x.view(2, x.shape[0] // 2, *x.shape[1:]) for x in [q, k, v]]
723
        if attn_bias is not None:
724
            assert len(attn_bias.shape) == 4, (
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                "Only support bias shape of [b, h, sq, sk] for forward, "
                "and [1, h, sq, sk] for backward!"
            )
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            assert (
                attn_bias.shape[-2] % 2 == 0 and attn_bias.shape[-1] % (2 * cp_size) == 0
            ), "Sequence length does not meet divisible requirements!"
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            # [b, np, sq, sk] -> [b, np, 2, sq//2, 2*cp, sk//(2*cp)]
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            attn_bias_ = attn_bias.view(
                *attn_bias.shape[:-2],
                2,
                attn_bias.shape[-2] // 2,
                2 * cp_size,
                attn_bias.shape[-1] // (2 * cp_size),
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            )
            # [b, np, sq, sk] -> [b, np, sq, 2*cp, sk//(2*cp)]
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            attn_bias = attn_bias.view(
                *attn_bias.shape[:-1], 2 * cp_size, attn_bias.shape[-1] // (2 * cp_size)
742
            )
743
        assert q.shape[-1] % 8 == 0, "hidden size per attention head should be multiple of 8"
744

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749
        softmax_lse_in_packed_format = False
        if qkv_format == "thd":
            if use_fused_attention:
                softmax_lse_in_packed_format = get_cudnn_version() >= (9, 6, 0)
            else:
750
                softmax_lse_in_packed_format = fa_utils.v2_6_0_plus or use_flash_attn_3
751

752
        flash_attn_fwd = None
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754
        if not use_fused_attention:
            fa_forward_kwargs = {"softmax_scale": softmax_scale}
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            if use_flash_attn_3:
                flash_attn_fwd = (
                    _flash_attn_fwd_v3  # pylint: disable=possibly-used-before-assignment
                )
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                fa_forward_kwargs["window_size"] = (-1, 0) if causal else (-1, -1)
            else:
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                if qkv_format == "thd":
                    flash_attn_fwd = _flash_attn_varlen_fwd
                else:
                    flash_attn_fwd = _flash_attn_fwd
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                fa_forward_kwargs["dropout_p"] = dropout_p
                fa_forward_kwargs["return_softmax"] = False
767
                if fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus:
768
                    fa_forward_kwargs["window_size"] = (-1, 0) if causal else (-1, -1)
769
                elif fa_utils.v2_7_0_plus:
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771
                    fa_forward_kwargs["window_size_left"] = -1
                    fa_forward_kwargs["window_size_right"] = 0 if causal else -1
772
                if fa_utils.v2_4_plus:
773
                    fa_forward_kwargs["alibi_slopes"] = None
774
                if fa_utils.v2_5_7_plus and qkv_format == "thd":
775
                    fa_forward_kwargs["block_table"] = None
776
                if fa_utils.v2_6_0_plus:
777
                    fa_forward_kwargs["softcap"] = 0.0
778

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        # Flash Attn inputs
        q_inputs = [None, None]
        kv_inputs = [None, None]
782
        attn_bias_inputs = [None, None]
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        # 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)]
787
        attn_biases = [None for _ in range(cp_size)]
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794

        # 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)]
795
        if qkv_format in ["bshd", "sbhd"]:
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            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)
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        send_recv_reqs = [[], []]

801
        out = None
802
        for i in range(cp_size + 1):
803
            if i < cp_size:
804
                with torch.cuda.stream(flash_attn_streams[i % 2]):
805
                    # wait until KV is received
806
                    for req in send_recv_reqs[(i + 1) % 2]:
807
808
                        req.wait()

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820
                    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,
                        )

821
                    if not fp8 or is_input_fp8 or int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
822
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824
                        kv_inputs[i % 2] = p2p_comm_buffers[i]
                    else:
                        # KV exchange is in BF16/FP16, cast received KV in each step
825
                        kv_inputs[i % 2] = QKV_quantizer(p2p_comm_buffers[i])._data
826
827
                    if causal:
                        if i == 0:
828
                            if pad_between_seqs:
829
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831
832
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834
                                cu_seqlens_q_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_q, cu_seqlens_q_padded, cp_size, rank, True, True
                                )
                                cu_seqlens_kv_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_kv, cu_seqlens_kv_padded, cp_size, rank, True, True
                                )
835
836
                            elif qkv_format == "thd":
                                cu_seqlens_q_per_step[i] = cu_seqlens_q // cp_size
837
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
838
839
840
                            else:
                                cu_seqlens_q_per_step[i] = cu_seqlens_q
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv
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856
                            if qkv_format == "bshd":
                                # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                                q_inputs[i % 2] = q.view(q.shape[0], -1, *q.shape[-2:])
                                # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
                                kv_inputs[i % 2] = kv_inputs[i % 2].view(
                                    k.shape[0], -1, 2, *k.shape[-2:]
                                )
                            elif qkv_format == "sbhd":
                                # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                                q_inputs[i % 2] = q.view(-1, *q.shape[-3:])
                                # [2, sk//2, b, 2, np, hn] -> [sk, b, 2, np, hn]
                                kv_inputs[i % 2] = kv_inputs[i % 2].view(
                                    -1, k.shape[2], 2, *k.shape[-2:]
                                )
                            elif qkv_format == "thd":
                                q_inputs[i % 2] = q
857
                            if use_fused_attention:
858
859
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
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865
                                    attn_bias_inputs[i % 2] = torch.cat(
                                        (
                                            attn_bias[..., idx, :],
                                            attn_bias[..., (2 * cp_size - idx - 1), :],
                                        ),
                                        dim=-1,
866
                                    ).contiguous()
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                                q_part = q_inputs[i % 2]
                                k_part = (
                                    kv_inputs[i % 2][..., 0, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][0]
                                )
                                v_part = (
                                    kv_inputs[i % 2][..., 1, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][1]
                                )
879
                                fp8_meta_kwargs = {}
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                                if fp8:
                                    q_part = QKV_quantizer.create_tensor_from_data(
                                        q_part, fake_dtype=qkv_dtype, internal=True
                                    )
                                    k_part = QKV_quantizer.create_tensor_from_data(
                                        k_part, fake_dtype=qkv_dtype, internal=True
                                    )
                                    v_part = QKV_quantizer.create_tensor_from_data(
                                        v_part, fake_dtype=qkv_dtype, internal=True
                                    )
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                                    fp8_meta_kwargs["s_quantizer"] = S_quantizer_per_step[i]
                                    fp8_meta_kwargs["o_quantizer"] = O_CP_quantizer_per_step[i]
892

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                                out_per_step[i], aux_ctx_tensors = fused_attn_fwd(
                                    is_training,
                                    max_seqlen_q,
                                    max_seqlen_kv,
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
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                                    q_part,
                                    k_part,
                                    v_part,
                                    fake_dtype=qkv_dtype,
                                    fused_attention_backend=fused_attn_backend,
904
905
906
907
908
909
910
911
912
                                    attn_scale=softmax_scale,
                                    dropout=dropout_p,
                                    qkv_layout=qkv_layout,
                                    attn_mask_type=attn_mask_type,
                                    attn_bias_type=attn_bias_type,
                                    attn_bias=attn_bias_inputs[i % 2],
                                    cu_seqlens_q_padded=cu_seqlens_q_padded,
                                    cu_seqlens_kv_padded=cu_seqlens_kv_padded,
                                    **fp8_meta_kwargs,
913
                                )
914
915
916
917
918
                                if fp8:
                                    softmax_lse_per_step[i], _, rng_states[i] = aux_ctx_tensors
                                else:
                                    softmax_lse_per_step[i], rng_states[i], *rest = aux_ctx_tensors
                                    attn_biases[i] = rest[0] if len(rest) > 0 else None
919
                            else:
920
921
922
923
924
925
926
927
928
                                fa_forward_args_thd = get_fa_args(
                                    True,
                                    use_flash_attn_3,
                                    qkv_format,
                                    cu_seqlens_q=cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv=cu_seqlens_kv_per_step[i],
                                    max_seqlen_q=max_seqlen_q,
                                    max_seqlen_kv=max_seqlen_kv,
                                )
929
                                fa_outputs = flash_attn_fwd(
930
                                    q_inputs[i % 2],
931
932
933
934
935
936
937
938
939
940
941
                                    (
                                        kv_inputs[i % 2][..., 0, :, :]
                                        if qkv_format in ["bshd", "sbhd"]
                                        else kv_inputs[i % 2][0]
                                    ),
                                    (
                                        kv_inputs[i % 2][..., 1, :, :]
                                        if qkv_format in ["bshd", "sbhd"]
                                        else kv_inputs[i % 2][1]
                                    ),
                                    *fa_forward_args_thd,
942
                                    causal=True,
943
                                    **fa_forward_kwargs,
944
                                )
945
                                if not fa_utils.v2_7_0_plus:
946
947
                                    out_per_step[i] = fa_outputs[4]
                                    softmax_lse_per_step[i] = fa_outputs[5]
948
                                    if not use_flash_attn_3:
949
950
951
952
                                        rng_states[i] = fa_outputs[7]
                                else:
                                    out_per_step[i] = fa_outputs[0]
                                    softmax_lse_per_step[i] = fa_outputs[1]
953
                                    if not use_flash_attn_3:
954
                                        rng_states[i] = fa_outputs[3]
955
                        elif i <= rank:
956
                            if pad_between_seqs:
957
958
959
960
961
962
963
964
965
966
967
                                cu_seqlens_q_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_q, cu_seqlens_q_padded, cp_size, rank, True, True
                                )
                                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,
                                )
968
969
                            elif qkv_format == "thd":
                                cu_seqlens_q_per_step[i] = cu_seqlens_q // cp_size
970
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // (cp_size * 2)
971
972
973
                            else:
                                cu_seqlens_q_per_step[i] = cu_seqlens_q
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv_half
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
                            if qkv_format == "bshd":
                                # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                                q_inputs[i % 2] = q.view(q.shape[0], -1, *q.shape[-2:])
                                # [b, 2, sk//2, 2, np, hn] -> [b, sk//2, 2, np, hn]
                                kv_inputs[i % 2] = kv_inputs[i % 2][:, 0, ...]
                            elif qkv_format == "sbhd":
                                # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                                q_inputs[i % 2] = q.view(-1, *q.shape[-3:])
                                # [2, sk//2, b, 2, np, hn] -> [sk//2, b, 2, np, hn]
                                kv_inputs[i % 2] = kv_inputs[i % 2][0]
                            elif qkv_format == "thd":
                                q_inputs[i % 2] = q
                                # [2, t, np, hn] -> [2, t/2, np, hn]
                                kv_inputs[i % 2] = tex.thd_read_half_tensor(
                                    kv_inputs[i % 2], cu_seqlens_kv_padded, 0
                                )
990
                            if use_fused_attention:
991
                                kv_inputs[i % 2] = kv_inputs[i % 2].contiguous()
992
993
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
994
                                    attn_bias_inputs[i % 2] = attn_bias[..., idx, :].contiguous()
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006

                                q_part = q_inputs[i % 2]
                                k_part = (
                                    kv_inputs[i % 2][..., 0, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][0]
                                )
                                v_part = (
                                    kv_inputs[i % 2][..., 1, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][1]
                                )
1007
                                fp8_meta_kwargs = {}
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
                                if fp8:
                                    q_part = QKV_quantizer.create_tensor_from_data(
                                        q_part, fake_dtype=qkv_dtype, internal=True
                                    )
                                    k_part = QKV_quantizer.create_tensor_from_data(
                                        k_part, fake_dtype=qkv_dtype, internal=True
                                    )
                                    v_part = QKV_quantizer.create_tensor_from_data(
                                        v_part, fake_dtype=qkv_dtype, internal=True
                                    )
1018
1019
                                    fp8_meta_kwargs["s_quantizer"] = S_quantizer_per_step[i]
                                    fp8_meta_kwargs["o_quantizer"] = O_CP_quantizer_per_step[i]
1020
1021
1022
1023
1024
1025
                                out_per_step[i], aux_ctx_tensors = fused_attn_fwd(
                                    is_training,
                                    max_seqlen_q,
                                    max_seqlen_kv // 2,
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
1026
1027
1028
1029
                                    q_part,
                                    k_part,
                                    v_part,
                                    qkv_dtype,
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
                                    fused_attn_backend,
                                    attn_scale=softmax_scale,
                                    dropout=dropout_p,
                                    qkv_layout=qkv_layout,
                                    attn_mask_type="padding" if padding else "no_mask",
                                    attn_bias_type=attn_bias_type,
                                    attn_bias=attn_bias_inputs[i % 2],
                                    cu_seqlens_q_padded=cu_seqlens_q_padded,
                                    cu_seqlens_kv_padded=(
                                        None
                                        if cu_seqlens_kv_padded is None
                                        else cu_seqlens_kv_padded // 2
                                    ),
                                    **fp8_meta_kwargs,
1044
                                )
1045
1046
1047
1048
1049
                                if fp8:
                                    softmax_lse_per_step[i], _, rng_states[i] = aux_ctx_tensors
                                else:
                                    softmax_lse_per_step[i], rng_states[i], *rest = aux_ctx_tensors
                                    attn_biases[i] = rest[0] if len(rest) > 0 else None
1050
                            else:
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
                                fa_forward_args_thd = get_fa_args(
                                    True,
                                    use_flash_attn_3,
                                    qkv_format,
                                    cu_seqlens_q=cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv=cu_seqlens_kv_per_step[i],
                                    max_seqlen_q=max_seqlen_q,
                                    max_seqlen_kv=max_seqlen_kv // 2,
                                )
                                if use_flash_attn_3 or (
1061
                                    fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus
1062
                                ):
1063
                                    fa_forward_kwargs["window_size"] = (-1, -1)
1064
                                elif fa_utils.v2_7_0_plus:
1065
1066
                                    fa_forward_kwargs["window_size_left"] = -1
                                    fa_forward_kwargs["window_size_right"] = -1
1067
                                fa_outputs = flash_attn_fwd(
1068
                                    q_inputs[i % 2],
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
                                    (
                                        kv_inputs[i % 2][..., 0, :, :]
                                        if qkv_format in ["bshd", "sbhd"]
                                        else kv_inputs[i % 2][0]
                                    ),
                                    (
                                        kv_inputs[i % 2][..., 1, :, :]
                                        if qkv_format in ["bshd", "sbhd"]
                                        else kv_inputs[i % 2][1]
                                    ),
                                    *fa_forward_args_thd,
1080
                                    causal=False,
1081
                                    **fa_forward_kwargs,
1082
                                )
1083
                                if not fa_utils.v2_7_0_plus:
1084
1085
                                    out_per_step[i] = fa_outputs[4]
                                    softmax_lse_per_step[i] = fa_outputs[5]
1086
                                    if not use_flash_attn_3:
1087
1088
1089
1090
                                        rng_states[i] = fa_outputs[7]
                                else:
                                    out_per_step[i] = fa_outputs[0]
                                    softmax_lse_per_step[i] = fa_outputs[1]
1091
                                    if not use_flash_attn_3:
1092
                                        rng_states[i] = fa_outputs[3]
1093
                        else:
1094
                            if pad_between_seqs:
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
                                cu_seqlens_q_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_q, cu_seqlens_q_padded, cp_size, rank, False, True
                                )
                                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,
                                )
1106
1107
                            elif qkv_format == "thd":
                                cu_seqlens_q_per_step[i] = cu_seqlens_q // (cp_size * 2)
1108
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
1109
1110
1111
                            else:
                                cu_seqlens_q_per_step[i] = cu_seqlens_q_half
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
                            if qkv_format == "bshd":
                                # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                                q_inputs[i % 2] = q[:, 1, ...]
                                # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
                                kv_inputs[i % 2] = kv_inputs[i % 2].view(
                                    k.shape[0], -1, 2, *k.shape[-2:]
                                )
                            elif qkv_format == "sbhd":
                                # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                                q_inputs[i % 2] = q[1]
                                # [2, sk//2, b, 2, np, hn] -> [sk, b, 2, np, hn]
                                kv_inputs[i % 2] = kv_inputs[i % 2].view(
                                    -1, k.shape[2], 2, *k.shape[-2:]
                                )
                            elif qkv_format == "thd":
                                # [t, np, hn] -> [t/2, np, hn]
                                q_inputs[i % 2] = tex.thd_read_half_tensor(
                                    q, cu_seqlens_q_padded, 1
                                )
1131
                            if use_fused_attention:
1132
                                q_inputs[i % 2] = q_inputs[i % 2].contiguous()
1133
1134
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
1135
1136
1137
1138
1139
1140
                                    attn_bias_inputs[i % 2] = torch.cat(
                                        (
                                            attn_bias_[..., 1, :, idx, :],
                                            attn_bias_[..., 1, :, (2 * cp_size - idx - 1), :],
                                        ),
                                        dim=-1,
1141
                                    ).contiguous()
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153

                                q_part = q_inputs[i % 2]
                                k_part = (
                                    kv_inputs[i % 2][..., 0, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][0]
                                )
                                v_part = (
                                    kv_inputs[i % 2][..., 1, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][1]
                                )
1154
                                fp8_meta_kwargs = {}
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
                                if fp8:
                                    q_part = QKV_quantizer.create_tensor_from_data(
                                        q_part, fake_dtype=qkv_dtype, internal=True
                                    )
                                    k_part = QKV_quantizer.create_tensor_from_data(
                                        k_part, fake_dtype=qkv_dtype, internal=True
                                    )
                                    v_part = QKV_quantizer.create_tensor_from_data(
                                        v_part, fake_dtype=qkv_dtype, internal=True
                                    )
1165
1166
                                    fp8_meta_kwargs["s_quantizer"] = S_quantizer_per_step[i]
                                    fp8_meta_kwargs["o_quantizer"] = O_CP_quantizer_per_step[i]
1167
1168
1169
1170
1171
1172
                                out_per_step[i], aux_ctx_tensors = fused_attn_fwd(
                                    is_training,
                                    max_seqlen_q // 2,
                                    max_seqlen_kv,
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
1173
1174
1175
1176
                                    q_part,
                                    k_part,
                                    v_part,
                                    qkv_dtype,
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
                                    fused_attn_backend,
                                    attn_scale=softmax_scale,
                                    dropout=dropout_p,
                                    qkv_layout=qkv_layout,
                                    attn_mask_type="padding" if padding else "no_mask",
                                    attn_bias_type=attn_bias_type,
                                    attn_bias=attn_bias_inputs[i % 2],
                                    cu_seqlens_q_padded=(
                                        None
                                        if cu_seqlens_q_padded is None
                                        else cu_seqlens_q_padded // 2
                                    ),
                                    cu_seqlens_kv_padded=cu_seqlens_kv_padded,
                                    **fp8_meta_kwargs,
1191
                                )
1192
1193
1194
1195
1196
                                if fp8:
                                    softmax_lse_per_step[i], _, rng_states[i] = aux_ctx_tensors
                                else:
                                    softmax_lse_per_step[i], rng_states[i], *rest = aux_ctx_tensors
                                    attn_biases[i] = rest[0] if len(rest) > 0 else None
1197
                            else:
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
                                fa_forward_args_thd = get_fa_args(
                                    True,
                                    use_flash_attn_3,
                                    qkv_format,
                                    cu_seqlens_q=cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv=cu_seqlens_kv_per_step[i],
                                    max_seqlen_q=max_seqlen_q // 2,
                                    max_seqlen_kv=max_seqlen_kv,
                                )
                                if use_flash_attn_3 or (
1208
                                    fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus
1209
                                ):
1210
                                    fa_forward_kwargs["window_size"] = (-1, -1)
1211
                                elif fa_utils.v2_7_0_plus:
1212
1213
                                    fa_forward_kwargs["window_size_left"] = -1
                                    fa_forward_kwargs["window_size_right"] = -1
1214
                                fa_outputs = flash_attn_fwd(
1215
                                    q_inputs[i % 2],
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
                                    (
                                        kv_inputs[i % 2][..., 0, :, :]
                                        if qkv_format in ["bshd", "sbhd"]
                                        else kv_inputs[i % 2][0]
                                    ),
                                    (
                                        kv_inputs[i % 2][..., 1, :, :]
                                        if qkv_format in ["bshd", "sbhd"]
                                        else kv_inputs[i % 2][1]
                                    ),
                                    *fa_forward_args_thd,
1227
                                    causal=False,
1228
                                    **fa_forward_kwargs,
1229
                                )
1230
                                if not fa_utils.v2_7_0_plus:
1231
1232
                                    out_per_step[i] = fa_outputs[4]
                                    softmax_lse_per_step[i] = fa_outputs[5]
1233
                                    if not use_flash_attn_3:
1234
1235
1236
1237
                                        rng_states[i] = fa_outputs[7]
                                else:
                                    out_per_step[i] = fa_outputs[0]
                                    softmax_lse_per_step[i] = fa_outputs[1]
1238
                                    if not use_flash_attn_3:
1239
                                        rng_states[i] = fa_outputs[3]
1240
                    else:
1241
                        if pad_between_seqs:
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
                            cu_seqlens_q_per_step[i] = get_cu_seqlens_on_cp_rank(
                                cu_seqlens_q, cu_seqlens_q_padded, cp_size, rank, True, True
                            )
                            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,
                            )
1253
1254
                        elif qkv_format == "thd":
                            cu_seqlens_q_per_step[i] = cu_seqlens_q // cp_size
1255
                            cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
1256
1257
1258
                        else:
                            cu_seqlens_q_per_step[i] = cu_seqlens_q
                            cu_seqlens_kv_per_step[i] = cu_seqlens_kv
1259
                        if use_fused_attention:
1260
1261
                            if attn_bias is not None:
                                idx = (rank - i) % cp_size
1262
1263
1264
1265
1266
1267
                                attn_bias_inputs[i % 2] = torch.cat(
                                    (
                                        attn_bias[..., idx, :],
                                        attn_bias[..., (2 * cp_size - idx - 1), :],
                                    ),
                                    dim=-1,
1268
                                ).contiguous()
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280

                            q_part = q
                            k_part = (
                                kv_inputs[i % 2][..., 0, :, :]
                                if qkv_format in ["bshd", "sbhd"]
                                else kv_inputs[i % 2][0]
                            )
                            v_part = (
                                kv_inputs[i % 2][..., 1, :, :]
                                if qkv_format in ["bshd", "sbhd"]
                                else kv_inputs[i % 2][1]
                            )
1281
                            fp8_meta_kwargs = {}
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
                            if fp8:
                                q_part = QKV_quantizer.create_tensor_from_data(
                                    q_part, fake_dtype=qkv_dtype, internal=True
                                )
                                k_part = QKV_quantizer.create_tensor_from_data(
                                    k_part, fake_dtype=qkv_dtype, internal=True
                                )
                                v_part = QKV_quantizer.create_tensor_from_data(
                                    v_part, fake_dtype=qkv_dtype, internal=True
                                )
1292
1293
                                fp8_meta_kwargs["s_quantizer"] = S_quantizer_per_step[i]
                                fp8_meta_kwargs["o_quantizer"] = O_CP_quantizer_per_step[i]
1294
1295
1296
1297
1298
1299
                            out_per_step[i], aux_ctx_tensors = fused_attn_fwd(
                                is_training,
                                max_seqlen_q,
                                max_seqlen_kv,
                                cu_seqlens_q_per_step[i],
                                cu_seqlens_kv_per_step[i],
1300
1301
1302
1303
                                q_part,
                                k_part,
                                v_part,
                                qkv_dtype,
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
                                fused_attn_backend,
                                attn_scale=softmax_scale,
                                dropout=dropout_p,
                                qkv_layout=qkv_layout,
                                attn_mask_type=attn_mask_type,
                                attn_bias_type=attn_bias_type,
                                attn_bias=attn_bias_inputs[i % 2],
                                cu_seqlens_q_padded=cu_seqlens_q_padded,
                                cu_seqlens_kv_padded=cu_seqlens_kv_padded,
                                **fp8_meta_kwargs,
1314
                            )
1315
1316
1317
1318
1319
                            if fp8:
                                softmax_lse_per_step[i], _, rng_states[i] = aux_ctx_tensors
                            else:
                                softmax_lse_per_step[i], rng_states[i], *rest = aux_ctx_tensors
                                attn_biases[i] = rest[0] if len(rest) > 0 else None
1320
                        else:
1321
1322
1323
1324
1325
1326
1327
1328
1329
                            fa_forward_args_thd = get_fa_args(
                                True,
                                use_flash_attn_3,
                                qkv_format,
                                cu_seqlens_q=cu_seqlens_q_per_step[i],
                                cu_seqlens_kv=cu_seqlens_kv_per_step[i],
                                max_seqlen_q=max_seqlen_q,
                                max_seqlen_kv=max_seqlen_kv,
                            )
1330
                            fa_outputs = flash_attn_fwd(
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
                                q,
                                (
                                    kv_inputs[i % 2][..., 0, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][0]
                                ),
                                (
                                    kv_inputs[i % 2][..., 1, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][1]
                                ),
                                *fa_forward_args_thd,
1343
                                causal=False,
1344
                                **fa_forward_kwargs,
1345
                            )
1346
                            if not fa_utils.v2_7_0_plus:
1347
1348
                                out_per_step[i] = fa_outputs[4]
                                softmax_lse_per_step[i] = fa_outputs[5]
1349
                                if not use_flash_attn_3:
1350
1351
1352
1353
                                    rng_states[i] = fa_outputs[7]
                            else:
                                out_per_step[i] = fa_outputs[0]
                                softmax_lse_per_step[i] = fa_outputs[1]
1354
                                if not use_flash_attn_3:
1355
                                    rng_states[i] = fa_outputs[3]
1356
1357
1358
1359

            if i > 0:
                # wait until fwd restuls correction of last step is done
                if i > 1:
1360
                    flash_attn_streams[(i - 1) % 2].wait_event(fwd_results_correction_done)
1361

1362
                with torch.cuda.stream(flash_attn_streams[(i - 1) % 2]):
1363
1364
1365
1366
1367
1368
1369
1370
                    if use_fused_attention:
                        # [b, np, sq, 1] -> [b, np, sq] or
                        # [t, np, 1] -> [t, np]
                        softmax_lse_per_step[i - 1].squeeze_(-1)
                        if softmax_lse_in_packed_format:
                            softmax_lse_per_step[i - 1] = (
                                softmax_lse_per_step[i - 1].transpose(0, 1).contiguous()
                            )
1371
                    if fp8:
1372
                        out_per_step[i - 1] = out_per_step[i - 1].dequantize(dtype=torch.float32)
1373
1374
                    if i == 1:
                        softmax_lse = torch.clone(softmax_lse_per_step[0]).to(torch.double)
1375
1376
                        if qkv_format == "thd":
                            out = torch.zeros_like(q if not fp8 else out_per_step[0]).view(q.shape)
1377
1378
1379
1380
                    elif (i - 1) <= rank or not causal:
                        flash_attn_fwd_softmax_lse_correction(
                            softmax_lse, softmax_lse_per_step[i - 1]
                        )
1381
                    else:
1382
                        if qkv_format == "thd":
1383
                            tex.thd_second_half_lse_correction(
1384
1385
1386
                                softmax_lse,
                                softmax_lse_per_step[i - 1],
                                cu_seqlens_q_padded,
1387
                                softmax_lse_in_packed_format,
1388
                            )
1389
                        else:
1390
1391
1392
                            flash_attn_fwd_second_half_softmax_lse_correction(
                                softmax_lse.view(*softmax_lse.shape[:-1], 2, -1),
                                softmax_lse_per_step[i - 1],
1393
                            )
1394
1395

                if i < cp_size:
1396
                    flash_attn_streams[(i - 1) % 2].record_event(fwd_results_correction_done)
1397
1398
1399

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

1400
1401
1402
1403
        second_half_lse_seqlen = None
        if causal and rank < (cp_size - 1):
            second_half_lse_seqlen = softmax_lse_per_step[-1].shape[-1]

1404
1405
        softmax_lse = softmax_lse.to(torch.float)
        for i in range(cp_size):
1406
            if i <= rank or not causal:
1407
                if qkv_format in ["bshd", "sbhd"]:
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
                    if i == 0:
                        out = flash_attn_fwd_out_correction_init(
                            out_per_step[0],
                            softmax_lse,
                            softmax_lse_per_step[0],
                            seq_dim,
                        )
                        out = out.view(q.shape)
                    else:
                        flash_attn_fwd_out_correction(
                            out.view(*out_per_step[i].shape),
                            out_per_step[i],
                            softmax_lse,
                            softmax_lse_per_step[i],
                            seq_dim,
                        )
1424
                elif qkv_format == "thd":
1425
1426
1427
1428
1429
                    tex.thd_out_correction(
                        out,
                        out_per_step[i],
                        softmax_lse,
                        softmax_lse_per_step[i],
1430
                        cu_seqlens_q_padded,
1431
                        False,
1432
                        softmax_lse_in_packed_format,
1433
                    )
1434
            else:
1435
                if qkv_format in ["bshd", "sbhd"]:
1436
1437
                    flash_attn_fwd_second_half_out_correction(
                        out,
1438
                        out_per_step[i],
1439
                        softmax_lse,
1440
                        softmax_lse_per_step[i],
1441
                        seq_dim,
1442
                    )
1443
                elif qkv_format == "thd":
1444
1445
1446
1447
1448
                    tex.thd_out_correction(
                        out,
                        out_per_step[i],
                        softmax_lse,
                        softmax_lse_per_step[i],
1449
                        cu_seqlens_q_padded,
1450
                        True,
1451
                        softmax_lse_in_packed_format,
1452
                    )
1453
1454

        kv = p2p_comm_buffers[-1]
1455
1456
1457
1458
1459
1460
1461
1462
        if qkv_format == "bshd":
            out = out.view(out.shape[0], -1, *out.shape[-2:])
            ctx.batch_size = out.shape[0]
        elif qkv_format == "sbhd":
            out = out.view(-1, *out.shape[-3:])
            ctx.batch_size = out.shape[1]

        if cp_size_a2a > 1:
1463
            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_after_attn(cp_size_a2a, out.device)
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
            out = flash_attn_a2a_communicate(
                out, chunk_ids_for_a2a, seq_dim, cp_size_a2a, cp_group_a2a, cp_stream, False
            )
            if use_fused_attention:
                if qkv_format == "bshd":
                    # [b*s, np, hn] -> [b, s, np, hn]
                    out = out.view(ctx.batch_size, -1, *out.shape[-2:])
                elif qkv_format == "sbhd":
                    # [s*b, np, hn] -> [s, b, np, hn]
                    out = out.view(-1, ctx.batch_size, *out.shape[-2:])
        elif not use_fused_attention:
1475
            out = out.view(-1, *out.shape[-2:])
1476

1477
1478
        if fp8 and use_fused_attention:
            amax_cp_fwd = amax_per_step.amax(dim=1)
1479
1480
            S_quantizer.amax.copy_(amax_cp_fwd[0])
            O_CP_quantizer.amax.copy_(amax_cp_fwd[1])
1481

1482
        out_fp8 = None
1483
        out_f16 = out.to(qkv_dtype)
1484

1485
        if fp8 and (is_output_fp8 or int(os.getenv("NVTE_FP8_DPA_BWD", "1"))):
1486
1487
1488
            out_fp8 = O_quantizer(out_f16)  # final result

        out_ret = out_fp8 if (fp8 and is_output_fp8) else out_f16
1489
1490

        if fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
1491
            q_save, kv_save, out_save = q, kv, out_fp8._data
1492
        elif fp8 and is_input_fp8:
1493
            q_save, kv_save, out_save = q, kv, out_f16
1494
        else:
1495
            q_f16 = q_f16.view(q.shape)
1496
1497
            q_save, kv_save, out_save = q_f16, kv, out_f16

1498
        tensors_to_save, tensor_objects = prepare_for_saving(
1499
1500
1501
            q_save,
            kv_save,
            out_save,
1502
            softmax_lse,
1503
1504
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
1505
1506
            *cu_seqlens_q_per_step,
            *cu_seqlens_kv_per_step,
1507
1508
            *rng_states,
            *attn_biases,
1509
        )
1510
1511
1512
        ctx.save_for_backward(*tensors_to_save)
        ctx.tensor_objects = tensor_objects

1513
1514
1515
        ctx.cp_group_a2a = cp_group_a2a
        ctx.cp_size_a2a = cp_size_a2a
        ctx.rank_a2a = rank_a2a
1516
1517
        ctx.cp_group = cp_group
        ctx.cp_global_ranks = cp_global_ranks
1518
        ctx.cp_stream = cp_stream
1519
1520
        ctx.dropout_p = dropout_p
        ctx.max_seqlen_q = max_seqlen_q
1521
        ctx.max_seqlen_kv = max_seqlen_kv
1522
        ctx.softmax_scale = softmax_scale
1523
        ctx.qkv_format = qkv_format
1524
        ctx.attn_mask_type = attn_mask_type
1525
1526
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_bias_shape = None if attn_bias is None else attn_bias.shape
1527
        ctx.deterministic = deterministic
1528
        ctx.use_fused_attention = use_fused_attention
1529
        ctx.softmax_lse_in_packed_format = softmax_lse_in_packed_format
1530
        ctx.second_half_lse_seqlen = second_half_lse_seqlen
1531
1532
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        ctx.fp8_meta = fp8_meta
1533
1534
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
1535
        ctx.use_flash_attn_3 = use_flash_attn_3
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551

        ctx.qkv_dtype = qkv_dtype
        ctx.dQKV_quantizer = dQKV_quantizer
        ctx.dQKV_CP_quantizer = dQKV_CP_quantizer
        ctx.dO_quantizer = dO_quantizer
        ctx.dP_quantizer = dP_quantizer
        ctx.QKV_quantizer = QKV_quantizer
        ctx.O_quantizer = O_quantizer
        ctx.S_quantizer = S_quantizer
        if ctx.fp8:
            ctx.QKV_quantizer = QKV_quantizer.copy()
            ctx.QKV_quantizer.scale = QKV_quantizer.scale.clone()
            ctx.O_quantizer = O_quantizer.copy()
            ctx.O_quantizer.scale = O_quantizer.scale.clone()
            ctx.S_quantizer = S_quantizer.copy()
            ctx.S_quantizer.scale = S_quantizer.scale.clone()
1552
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndKVP2P.forward")
1553

1554
        return out_ret
1555
1556
1557

    @staticmethod
    def backward(ctx, dout):
1558
        # pylint: disable=missing-function-docstring
1559
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndKVP2P.backward")
1560
1561
1562
        cp_size_a2a = ctx.cp_size_a2a
        rank_a2a = ctx.rank_a2a

1563
1564
        cp_size = get_distributed_world_size(ctx.cp_group)
        rank = get_distributed_rank(ctx.cp_group)
1565
1566
        send_dst = ctx.cp_global_ranks[(rank - 1) % cp_size * cp_size_a2a + rank_a2a]
        recv_src = ctx.cp_global_ranks[(rank + 1) % cp_size * cp_size_a2a + rank_a2a]
1567
1568
        batch_p2p_comm = int(os.getenv("NVTE_BATCH_MHA_P2P_COMM", "0")) or (cp_size == 2)

1569
        q, kv, out, softmax_lse, cu_seqlens_q_padded, cu_seqlens_kv_padded, *other_tensors = (
1570
            restore_from_saved(ctx.tensor_objects, ctx.saved_tensors)
1571
1572
1573
1574
1575
        )
        cu_seqlens_q_per_step = other_tensors[:cp_size]
        cu_seqlens_kv_per_step = other_tensors[cp_size : cp_size * 2]
        rng_states = other_tensors[cp_size * 2 : cp_size * 3]
        attn_biases = other_tensors[cp_size * 3 : cp_size * 4]
1576

1577
1578
        causal = "causal" in ctx.attn_mask_type
        padding = "padding" in ctx.attn_mask_type
1579
1580

        seq_dim = None
1581
        if ctx.qkv_format in ["bshd", "sbhd"]:
1582
            seq_dim = ctx.qkv_format.index("s")
1583
1584
1585
            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
1586

1587
        if attn_biases[0] is not None:
1588
1589
            # [b, np, sq, 2*cp, sk//(2*cp)]
            attn_dbias = torch.zeros(
1590
                *ctx.attn_bias_shape, dtype=attn_biases[0].dtype, device=attn_biases[0].device
1591
1592
1593
            )
            # [b, np, sq, 2*cp, sk//(2*cp)] -> [b, np, 2, sq//2, 2*cp, sk//(2*cp)]
            attn_dbias_ = attn_dbias.view(
1594
                *attn_dbias.shape[:-3], 2, attn_dbias.shape[-3] // 2, *attn_dbias.shape[-2:]
1595
1596
1597
            )
        else:
            attn_dbias = None
1598
            attn_dbias_ = None
1599

1600
1601
        softmax_lse_ = None
        if causal and ctx.second_half_lse_seqlen is not None:
1602
            if ctx.qkv_format == "thd":
1603
                softmax_lse_ = tex.thd_read_second_half_lse(
1604
1605
1606
1607
                    softmax_lse,
                    cu_seqlens_q_padded,
                    ctx.softmax_lse_in_packed_format,
                    ctx.second_half_lse_seqlen,
1608
                )
1609
1610
            else:
                # [b, np, sq] -> [b, np, 2, sq//2]
1611
                softmax_lse_ = softmax_lse.view(*softmax_lse.shape[:-1], 2, -1)
1612
                softmax_lse_ = softmax_lse_[..., 1, :].contiguous()
1613
1614
1615
1616
1617
1618
            if ctx.use_fused_attention:
                if ctx.softmax_lse_in_packed_format:
                    softmax_lse_ = softmax_lse_.transpose(0, 1).contiguous()
                # [b, np, sq//2] -> [b, np, sq//2, 1] or
                # [t//2, np] -> [t//2, np, 1]
                softmax_lse_.unsqueeze_(-1)
1619
        if ctx.use_fused_attention:
1620
1621
1622
1623
            if ctx.softmax_lse_in_packed_format:
                softmax_lse = softmax_lse.transpose(0, 1).contiguous()
            # [b, np, sq] -> [b, np, sq, 1] or
            # [t, np] -> [t, np, 1]
1624
            softmax_lse.unsqueeze_(-1)
1625
            dout = dout.contiguous()
1626

1627
        dq = None
1628
        dout_dtype = dout.dtype
1629
1630
        fused_attn_backend = None
        fused_attn_dqkv_dtype = None
1631
1632
1633
        amax_per_step = None
        dP_quantizer_per_step = [None for _ in range(cp_size)]
        dQKV_CP_quantizer_per_step = [None for _ in range(cp_size)]
1634
1635
1636
        if ctx.fp8:
            if ctx.use_fused_attention:
                fused_attn_backend = FusedAttnBackend["FP8"]
1637

1638
                if ctx.is_output_fp8:
1639
                    assert isinstance(dout, Float8Tensor), "dout must be Float8Tensors for FP8 MHA!"
1640
                    ctx.dO_quantizer = dout._quantizer
1641
                else:
1642
                    dout = ctx.dO_quantizer(dout)
1643
1644
1645
1646
1647
1648
                fused_attn_dqkv_dtype = TE_DType[dout._data.dtype]
                dq_fp8 = torch.empty((cp_size, *q.shape), dtype=dout._data.dtype, device=q.device)
                dkv_fp8 = torch.empty(
                    (cp_size, *kv.shape), dtype=dout._data.dtype, device=kv.device
                )
                dkv_fp8_ = torch.empty_like(dkv_fp8)
1649
                p2p_comm_buffers = [[kv, dkv_fp8], [torch.empty_like(kv), dkv_fp8_]]
1650
                dout = dout._data
1651
                fp8_meta_kwargs = {}
1652
                fp8_meta_kwargs["s_quantizer"] = ctx.S_quantizer
1653
1654
1655
                amax_per_step = torch.zeros((2, cp_size), dtype=torch.float32, device=q.device)
                for i in range(cp_size):
                    dP_quantizer_per_step[i] = ctx.dP_quantizer.copy()
1656
                    dP_quantizer_per_step[i].amax = amax_per_step[0][i].reshape((1,))
1657
                    dQKV_CP_quantizer_per_step[i] = ctx.dQKV_CP_quantizer.copy()
1658
                    dQKV_CP_quantizer_per_step[i].amax = amax_per_step[1][i].reshape((1,))
1659
1660
1661
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
            if ctx.fp8_meta is not None:
                if ctx.is_input_fp8:
                    q = ctx.QKV_quantizer.create_tensor_from_data(
                        q, fake_dtype=ctx.qkv_dtype, internal=True
                    )
                    kv = ctx.QKV_quantizer.create_tensor_from_data(
                        kv, fake_dtype=ctx.qkv_dtype, internal=True
                    )
                    q = q.dequantize(dtype=ctx.qkv_dtype)
                    kv = kv.dequantize(dtype=ctx.qkv_dtype)
                if ctx.is_output_fp8:
                    assert isinstance(dout, Float8Tensor), "dout must be Float8Tensors for FP8 MHA!"
                    if cp_size_a2a == 1:
                        dout = dout.dequantize(dtype=dout_dtype)
                    else:
                        ctx.dO_quantizer = dout._quantizer
                        dout = dout._data
1679
1680
1681
1682
1683
1684
1685
1686
            dq = torch.empty_like(q)
            p2p_comm_buffers = [
                torch.empty((2, *kv.shape), dtype=kv.dtype, device=kv.device),
                torch.empty((2, *kv.shape), dtype=kv.dtype, device=kv.device),
            ]
            p2p_comm_buffers[0][0].copy_(kv)
            if ctx.use_fused_attention:
                fp8_meta_kwargs = {}
1687
                fused_attn_dqkv_dtype = TE_DType[dout_dtype]
1688
1689
                fused_attn_backend = FusedAttnBackend["F16_arbitrary_seqlen"]

1690
1691
1692
1693
        if cp_size_a2a > 1:
            if not ctx.use_fused_attention:
                out = out.view(ctx.batch_size, -1, *out.shape[-2:])
                dout = dout.view(*out.shape)
1694
1695
1696
            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_before_attn(
                cp_size_a2a, out.device
            )
1697
1698
1699
1700
1701
1702
1703
1704
1705
            out, dout = flash_attn_a2a_communicate(
                [out, dout],
                chunk_ids_for_a2a,
                seq_dim,
                cp_size_a2a,
                ctx.cp_group_a2a,
                ctx.cp_stream,
                True,
            )
1706
            if not ctx.fp8 and ctx.fp8_meta is not None and ctx.is_output_fp8:
1707
1708
1709
1710
                dout = ctx.dO_quantizer.create_tensor_from_data(
                    dout, fake_dtype=dout_dtype, internal=True
                )
                dout = dout.dequantize(dtype=dout_dtype)
1711

1712
1713
1714
1715
        out = out.view(*q.shape)
        dout = dout.view(*q.shape)
        send_recv_reqs = []

1716
        flash_attn_bwd = None
1717
1718
        if not ctx.use_fused_attention:
            fa_backward_kwargs = {"softmax_scale": ctx.softmax_scale}
1719
1720
1721
1722
            if ctx.use_flash_attn_3:
                flash_attn_bwd = (
                    _flash_attn_bwd_v3  # pylint: disable=possibly-used-before-assignment
                )
1723
1724
                fa_backward_kwargs["deterministic"] = ctx.deterministic
            else:
1725
1726
1727
1728
                if ctx.qkv_format == "thd":
                    flash_attn_bwd = _flash_attn_varlen_bwd
                else:
                    flash_attn_bwd = _flash_attn_bwd
1729
                fa_backward_kwargs["dropout_p"] = ctx.dropout_p
1730
                if fa_utils.v2_4_plus:
1731
                    fa_backward_kwargs["alibi_slopes"] = None
1732
                if fa_utils.v2_4_1_plus:
1733
                    fa_backward_kwargs["deterministic"] = ctx.deterministic
1734
                if fa_utils.v2_6_0_plus:
1735
                    fa_backward_kwargs["softcap"] = 0.0
1736

1737
1738
1739
1740
1741
        for i in range(cp_size):
            # wait until KV is received
            for req in send_recv_reqs:
                req.wait()

1742
1743
            send_tensor = p2p_comm_buffers[i % 2]
            recv_tensor = p2p_comm_buffers[(i + 1) % 2]
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
            if ctx.fp8:
                if i < cp_size - 1:
                    send_recv_reqs = flash_attn_p2p_communicate(
                        rank,
                        send_tensor[0],
                        send_dst,
                        recv_tensor[0],
                        recv_src,
                        ctx.cp_group,
                        batch_p2p_comm,
                    )
                else:
                    dkv_a2a_req = torch.distributed.all_to_all_single(
                        dkv_fp8,
                        dkv_fp8_,
                        group=ctx.cp_group,
                        async_op=True,
                    )
                    send_recv_reqs = [dkv_a2a_req]
            else:
                if i == 0:
                    send_tensor = send_tensor[0]
                    recv_tensor = recv_tensor[0]
                if i == (cp_size - 1):
                    send_tensor = send_tensor[1]
                    recv_tensor = recv_tensor[1]
                send_recv_reqs = flash_attn_p2p_communicate(
                    rank, send_tensor, send_dst, recv_tensor, recv_src, ctx.cp_group, batch_p2p_comm
                )
1773

1774
            kv = p2p_comm_buffers[i % 2][0]
1775
1776
            q_, kv_, out_, dout_ = None, None, None, None
            dq_, dk_, dv_ = None, None, None
1777
            # In reversed order of fwd
1778
            if causal:
1779
                if i == (cp_size - 1):
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
                    if ctx.qkv_format == "bshd":
                        # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                        q_, out_, dout_ = [
                            x.view(x.shape[0], -1, *x.shape[-2:]) for x in [q, out, dout]
                        ]
                        # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
                        kv_ = kv.view(kv.shape[0], -1, *kv.shape[-3:])
                    elif ctx.qkv_format == "sbhd":
                        # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                        q_, out_, dout_ = [x.view(-1, *x.shape[-3:]) for x in [q, out, dout]]
                        # [2, sk//2, b, 2, np, hn] -> [sk, b, 2, np, hn]
                        kv_ = kv.view(-1, *kv.shape[-4:])
                    elif ctx.qkv_format == "thd":
                        q_, kv_, out_, dout_ = q, kv, out, dout
1794
                    if ctx.use_fused_attention:
1795
1796
1797
1798
1799
1800
1801
1802
                        if ctx.fp8:
                            aux_ctx_tensors = [
                                softmax_lse,
                                softmax_lse,
                                rng_states[cp_size - i - 1],
                            ]
                        else:
                            aux_ctx_tensors = [softmax_lse, rng_states[cp_size - i - 1]]
1803
                        if attn_dbias is not None:
1804
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
                        q_part = q_
                        k_part = kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0]
                        v_part = kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1]
                        out_part = out_
                        dout_part = dout_

                        if ctx.fp8:
                            q_part = ctx.QKV_quantizer.create_tensor_from_data(
                                q_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            k_part = ctx.QKV_quantizer.create_tensor_from_data(
                                k_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            v_part = ctx.QKV_quantizer.create_tensor_from_data(
                                v_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            out_part = ctx.O_quantizer.create_tensor_from_data(
                                out_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            dout_part = ctx.dO_quantizer.create_tensor_from_data(
1825
                                dout_part, fake_dtype=dout_dtype, internal=True
1826
                            )
1827
1828
                            fp8_meta_kwargs["dp_quantizer"] = dP_quantizer_per_step[i]
                            fp8_meta_kwargs["dqkv_quantizer"] = dQKV_CP_quantizer_per_step[i]
1829
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
1830
                            ctx.max_seqlen_q,
1831
1832
1833
                            ctx.max_seqlen_kv,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
1834
1835
1836
1837
1838
                            q_part,
                            k_part,
                            v_part,
                            out_part,
                            dout_part,
1839
                            dout_dtype,
1840
                            fused_attn_dqkv_dtype,
1841
                            aux_ctx_tensors,
1842
                            fused_attn_backend,
1843
1844
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
                            cu_seqlens_kv_padded=cu_seqlens_kv_padded,
1845
1846
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
1847
                            qkv_layout=qkv_layout,
1848
                            attn_mask_type=ctx.attn_mask_type,
1849
                            attn_bias_type=ctx.attn_bias_type,
1850
1851
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
1852
                        )
1853
1854
1855
1856
                        if ctx.fp8:
                            dq_ = dq_._data
                            dk_ = dk_._data
                            dv_ = dv_._data
1857
                    else:
1858
                        dq_ = torch.empty_like(q_)
1859
                        dkv_ = torch.empty_like(kv_)
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
                        fa_backward_args_thd = get_fa_args(
                            False,
                            ctx.use_flash_attn_3,
                            ctx.qkv_format,
                            cu_seqlens_q=cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv=cu_seqlens_kv_per_step[cp_size - i - 1],
                            max_seqlen_q=ctx.max_seqlen_q,
                            max_seqlen_kv=ctx.max_seqlen_kv,
                            dq=dq_,
                            dk=(
                                dkv_[..., 0, :, :]
                                if ctx.qkv_format in ["bshd", "sbhd"]
                                else dkv_[0]
                            ),
                            dv=(
                                dkv_[..., 1, :, :]
                                if ctx.qkv_format in ["bshd", "sbhd"]
                                else dkv_[1]
                            ),
                        )
                        if ctx.use_flash_attn_3 or (
                            fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus
                        ):
1883
                            fa_backward_kwargs["window_size"] = (-1, 0)
1884
                        elif fa_utils.v2_7_0_plus:
1885
1886
                            fa_backward_kwargs["window_size_left"] = -1
                            fa_backward_kwargs["window_size_right"] = 0
1887
                        if not ctx.use_flash_attn_3:
1888
1889
                            fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                        flash_attn_bwd(
1890
1891
                            dout_,
                            q_,
1892
1893
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
1894
1895
                            out_,
                            softmax_lse,
1896
                            *fa_backward_args_thd,
1897
1898
                            causal=True,
                            **fa_backward_kwargs,
1899
                        )
1900
                elif i >= (cp_size - rank - 1):
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
                    if ctx.qkv_format == "bshd":
                        # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                        q_, out_, dout_ = [
                            x.view(x.shape[0], -1, *x.shape[-2:]) for x in [q, out, dout]
                        ]
                        # [b, 2, sk//2, 2, np, hn] -> [b, sk//2, 2, np, hn]
                        kv_ = kv[:, 0]
                    elif ctx.qkv_format == "sbhd":
                        # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                        q_, out_, dout_ = [x.view(-1, *x.shape[-3:]) for x in [q, out, dout]]
                        # [2, sk//2, b, 2, np, hn] -> [sk//2, b, 2, np, hn]
                        kv_ = kv[0]
                    elif ctx.qkv_format == "thd":
                        q_, out_, dout_ = q, out, dout
                        # [2, t, np, hn] -> [2, t/2, np, hn]
                        kv_ = tex.thd_read_half_tensor(kv, cu_seqlens_kv_padded, 0)
1917
                    if ctx.use_fused_attention:
1918
                        kv_ = kv_.contiguous()
1919
1920
1921
1922
1923
1924
1925
1926
                        if ctx.fp8:
                            aux_ctx_tensors = [
                                softmax_lse,
                                softmax_lse,
                                rng_states[cp_size - i - 1],
                            ]
                        else:
                            aux_ctx_tensors = [softmax_lse, rng_states[cp_size - i - 1]]
1927
                        if attn_dbias is not None:
1928
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
                        q_part = q_
                        k_part = kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0]
                        v_part = kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1]
                        out_part = out_
                        dout_part = dout_

                        if ctx.fp8:
                            q_part = ctx.QKV_quantizer.create_tensor_from_data(
                                q_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            k_part = ctx.QKV_quantizer.create_tensor_from_data(
                                k_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            v_part = ctx.QKV_quantizer.create_tensor_from_data(
                                v_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            out_part = ctx.O_quantizer.create_tensor_from_data(
                                out_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            dout_part = ctx.dO_quantizer.create_tensor_from_data(
1949
                                dout_part, fake_dtype=dout_dtype, internal=True
1950
                            )
1951
1952
                            fp8_meta_kwargs["dp_quantizer"] = dP_quantizer_per_step[i]
                            fp8_meta_kwargs["dqkv_quantizer"] = dQKV_CP_quantizer_per_step[i]
1953
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
1954
                            ctx.max_seqlen_q,
1955
1956
1957
                            ctx.max_seqlen_kv // 2,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
1958
1959
1960
1961
1962
                            q_part,
                            k_part,
                            v_part,
                            out_part,
                            dout_part,
1963
                            dout_dtype,
1964
                            fused_attn_dqkv_dtype,
1965
                            aux_ctx_tensors,
1966
                            fused_attn_backend,
1967
1968
1969
1970
                            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
                            ),
1971
1972
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
1973
                            qkv_layout=qkv_layout,
1974
                            attn_mask_type="padding" if padding else "no_mask",
1975
                            attn_bias_type=ctx.attn_bias_type,
1976
1977
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
1978
                        )
1979
1980
1981
1982
                        if ctx.fp8:
                            dq_ = dq_._data
                            dk_ = dk_._data
                            dv_ = dv_._data
1983
                    else:
1984
                        dq_ = torch.empty_like(q_)
1985
                        dkv_ = torch.empty_like(kv_)
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
                        fa_backward_args_thd = get_fa_args(
                            False,
                            ctx.use_flash_attn_3,
                            ctx.qkv_format,
                            cu_seqlens_q=cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv=cu_seqlens_kv_per_step[cp_size - i - 1],
                            max_seqlen_q=ctx.max_seqlen_q,
                            max_seqlen_kv=ctx.max_seqlen_kv // 2,
                            dq=dq_,
                            dk=(
                                dkv_[..., 0, :, :]
                                if ctx.qkv_format in ["bshd", "sbhd"]
                                else dkv_[0]
                            ),
                            dv=(
                                dkv_[..., 1, :, :]
                                if ctx.qkv_format in ["bshd", "sbhd"]
                                else dkv_[1]
                            ),
                        )
                        if ctx.use_flash_attn_3 or (
                            fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus
                        ):
2009
                            fa_backward_kwargs["window_size"] = (-1, -1)
2010
                        elif fa_utils.v2_7_0_plus:
2011
2012
                            fa_backward_kwargs["window_size_left"] = -1
                            fa_backward_kwargs["window_size_right"] = -1
2013
                        if not ctx.use_flash_attn_3:
2014
2015
                            fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                        flash_attn_bwd(
2016
2017
                            dout_,
                            q_,
2018
2019
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
2020
2021
                            out_,
                            softmax_lse,
2022
                            *fa_backward_args_thd,
2023
2024
                            causal=False,
                            **fa_backward_kwargs,
2025
2026
                        )
                else:
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
                    if ctx.qkv_format == "bshd":
                        # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                        q_, out_, dout_ = q[:, 1], out[:, 1], dout[:, 1]
                        # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
                        kv_ = kv.view(kv.shape[0], -1, *kv.shape[-3:])
                    elif ctx.qkv_format == "sbhd":
                        # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                        q_, out_, dout_ = q[1], out[1], dout[1]
                        # [2, sk//2, b, 2, np, hn] -> [sk, b, 2, np, hn]
                        kv_ = kv.view(-1, *kv.shape[-4:])
                    elif ctx.qkv_format == "thd":
                        # [t, np, hn] -> [t/2, np, hn]
                        q_, out_, dout_ = [
                            tex.thd_read_half_tensor(x, cu_seqlens_q_padded, 1)
                            for x in [q, out, dout]
                        ]
                        kv_ = kv
2044
                    if ctx.use_fused_attention:
2045
                        q_, out_, dout_ = [x.contiguous() for x in [q_, out_, dout_]]
2046
2047
2048
2049
2050
2051
2052
2053
                        if ctx.fp8:
                            aux_ctx_tensors = [
                                softmax_lse_,
                                softmax_lse_,
                                rng_states[cp_size - i - 1],
                            ]
                        else:
                            aux_ctx_tensors = [softmax_lse_, rng_states[cp_size - i - 1]]
2054
                        if attn_dbias is not None:
2055
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076

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

                        if ctx.fp8:
                            q_part = ctx.QKV_quantizer.create_tensor_from_data(
                                q_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            k_part = ctx.QKV_quantizer.create_tensor_from_data(
                                k_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            v_part = ctx.QKV_quantizer.create_tensor_from_data(
                                v_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            out_part = ctx.O_quantizer.create_tensor_from_data(
                                out_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            dout_part = ctx.dO_quantizer.create_tensor_from_data(
2077
                                dout_part, fake_dtype=dout_dtype, internal=True
2078
                            )
2079
2080
                            fp8_meta_kwargs["dp_quantizer"] = dP_quantizer_per_step[i]
                            fp8_meta_kwargs["dqkv_quantizer"] = dQKV_CP_quantizer_per_step[i]
2081
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2082
                            ctx.max_seqlen_q // 2,
2083
2084
2085
                            ctx.max_seqlen_kv,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2086
2087
2088
2089
2090
                            q_part,
                            k_part,
                            v_part,
                            out_part,
                            dout_part,
2091
                            dout_dtype,
2092
                            fused_attn_dqkv_dtype,
2093
                            aux_ctx_tensors,
2094
                            fused_attn_backend,
2095
2096
2097
2098
                            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,
2099
2100
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
2101
                            qkv_layout=qkv_layout,
2102
                            attn_mask_type="padding" if padding else "no_mask",
2103
                            attn_bias_type=ctx.attn_bias_type,
2104
2105
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
2106
                        )
2107
2108
2109
2110
                        if ctx.fp8:
                            dq_ = dq_._data
                            dk_ = dk_._data
                            dv_ = dv_._data
2111
                    else:
2112
                        dq_ = torch.empty_like(q_)
2113
                        dkv_ = torch.empty_like(kv_)
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
                        fa_backward_args_thd = get_fa_args(
                            False,
                            ctx.use_flash_attn_3,
                            ctx.qkv_format,
                            cu_seqlens_q=cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv=cu_seqlens_kv_per_step[cp_size - i - 1],
                            max_seqlen_q=ctx.max_seqlen_q // 2,
                            max_seqlen_kv=ctx.max_seqlen_kv,
                            dq=dq_,
                            dk=(
                                dkv_[..., 0, :, :]
                                if ctx.qkv_format in ["bshd", "sbhd"]
                                else dkv_[0]
                            ),
                            dv=(
                                dkv_[..., 1, :, :]
                                if ctx.qkv_format in ["bshd", "sbhd"]
                                else dkv_[1]
                            ),
                        )
                        if ctx.use_flash_attn_3 or (
                            fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus
                        ):
2137
                            fa_backward_kwargs["window_size"] = (-1, -1)
2138
                        elif fa_utils.v2_7_0_plus:
2139
2140
                            fa_backward_kwargs["window_size_left"] = -1
                            fa_backward_kwargs["window_size_right"] = -1
2141
                        if not ctx.use_flash_attn_3:
2142
2143
                            fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                        flash_attn_bwd(
2144
2145
                            dout_,
                            q_,
2146
2147
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
2148
2149
                            out_,
                            softmax_lse_,
2150
                            *fa_backward_args_thd,
2151
2152
                            causal=False,
                            **fa_backward_kwargs,
2153
2154
2155
                        )
            else:
                if ctx.use_fused_attention:
2156
2157
2158
2159
                    if ctx.fp8:
                        aux_ctx_tensors = [softmax_lse, softmax_lse, rng_states[cp_size - i - 1]]
                    else:
                        aux_ctx_tensors = [softmax_lse, rng_states[cp_size - i - 1]]
2160
                    if attn_dbias is not None:
2161
                        aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2162
2163
2164
2165
2166
2167
2168
2169
                    q_part = q
                    k_part = kv[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv[0]
                    v_part = kv[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv[1]
                    out_part = out
                    dout_part = dout

                    if ctx.fp8:
                        q_part = ctx.QKV_quantizer.create_tensor_from_data(
2170
                            q_part, fake_dtype=ctx.qkv_dtype, internal=True
2171
2172
                        )
                        k_part = ctx.QKV_quantizer.create_tensor_from_data(
2173
                            k_part, fake_dtype=ctx.qkv_dtype, internal=True
2174
2175
                        )
                        v_part = ctx.QKV_quantizer.create_tensor_from_data(
2176
                            v_part, fake_dtype=ctx.qkv_dtype, internal=True
2177
2178
                        )
                        out_part = ctx.O_quantizer.create_tensor_from_data(
2179
                            out_part, fake_dtype=ctx.qkv_dtype, internal=True
2180
2181
                        )
                        dout_part = ctx.dO_quantizer.create_tensor_from_data(
2182
                            dout_part, fake_dtype=dout_dtype, internal=True
2183
                        )
2184
2185
                        fp8_meta_kwargs["dp_quantizer"] = dP_quantizer_per_step[i]
                        fp8_meta_kwargs["dqkv_quantizer"] = dQKV_CP_quantizer_per_step[i]
2186
                    dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2187
                        ctx.max_seqlen_q,
2188
2189
2190
                        ctx.max_seqlen_kv,
                        cu_seqlens_q_per_step[cp_size - i - 1],
                        cu_seqlens_kv_per_step[cp_size - i - 1],
2191
2192
2193
2194
2195
                        q_part,
                        k_part,
                        v_part,
                        out_part,
                        dout_part,
2196
                        dout_dtype,
2197
                        fused_attn_dqkv_dtype,
2198
                        aux_ctx_tensors,
2199
                        fused_attn_backend,
2200
2201
                        cu_seqlens_q_padded=cu_seqlens_q_padded,
                        cu_seqlens_kv_padded=cu_seqlens_kv_padded,
2202
2203
                        attn_scale=ctx.softmax_scale,
                        dropout=ctx.dropout_p,
2204
                        qkv_layout=qkv_layout,
2205
                        attn_mask_type=ctx.attn_mask_type,
2206
                        attn_bias_type=ctx.attn_bias_type,
2207
2208
                        deterministic=ctx.deterministic,
                        **fp8_meta_kwargs,
2209
                    )
2210
2211
2212
2213
2214
2215

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

2216
                else:
2217
2218
                    dq_ = torch.empty_like(q)
                    dkv_ = torch.empty_like(kv)
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
                    fa_backward_args_thd = get_fa_args(
                        False,
                        ctx.use_flash_attn_3,
                        ctx.qkv_format,
                        cu_seqlens_q=cu_seqlens_q_per_step[cp_size - i - 1],
                        cu_seqlens_kv=cu_seqlens_kv_per_step[cp_size - i - 1],
                        max_seqlen_q=ctx.max_seqlen_q,
                        max_seqlen_kv=ctx.max_seqlen_kv,
                        dq=dq_,
                        dk=dkv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else dkv_[0],
                        dv=dkv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else dkv_[1],
                    )
                    if ctx.use_flash_attn_3 or (fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus):
2232
                        fa_backward_kwargs["window_size"] = (-1, -1)
2233
                    elif fa_utils.v2_7_0_plus:
2234
2235
                        fa_backward_kwargs["window_size_left"] = -1
                        fa_backward_kwargs["window_size_right"] = -1
2236
                    if not ctx.use_flash_attn_3:
2237
2238
                        fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                    flash_attn_bwd(
2239
2240
2241
2242
2243
                        dout,
                        q,
                        kv[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv[0],
                        kv[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv[1],
                        out,
2244
                        softmax_lse,
2245
                        *fa_backward_args_thd,
2246
2247
                        causal=False,
                        **fa_backward_kwargs,
2248
2249
                    )

2250
2251
            if ctx.fp8:
                dq = dq_fp8[(rank + i + 1) % cp_size]
2252
2253
2254
            if causal and ctx.qkv_format in ["bshd", "sbhd"] and i >= (cp_size - rank - 1):
                # [b, sq, np, hn] -> [b, 2, sq//2, np, hn] or
                # [sq, b, np, hn] -> [2, sq//2, b, np, hn]
2255
                dq_ = dq_.view(*dq.shape)
2256

2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
            if ctx.fp8:
                if i >= (cp_size - rank - 1) or not causal:
                    dq.copy_(dq_)
                else:
                    if ctx.qkv_format == "bshd":
                        dq[:, 0, ...].fill_(0)
                        dq[:, 1, ...].copy_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[0].fill_(0)
                        dq[1].copy_(dq_)
            elif causal:
2268
                if i > (cp_size - rank - 1):
2269
                    dq.add_(dq_)
2270
2271
                elif i == (cp_size - rank - 1):
                    if rank == (cp_size - 1):
2272
2273
                        dq.copy_(dq_)
                    else:
2274
2275
2276
2277
2278
2279
                        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])
2280
                        elif ctx.qkv_format == "thd":
2281
                            tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "copy", "add")
2282
                elif i > 0:
2283
2284
2285
2286
                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].add_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].add_(dq_)
2287
                    elif ctx.qkv_format == "thd":
2288
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "none", "add")
2289
                else:
2290
2291
2292
2293
                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].copy_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].copy_(dq_)
2294
                    elif ctx.qkv_format == "thd":
2295
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "none", "copy")
2296
2297
2298
2299
2300
            else:
                if i == 0:
                    dq.copy_(dq_)
                else:
                    dq.add_(dq_)
2301

2302
            if attn_dbias is not None:
2303
                idx = (rank + i + 1) % cp_size
2304
                if i == (cp_size - 1) or not causal:
2305
                    # [b, np, sq, sk//cp] -> [b, np, sq, 2, sk//(2*cp)]
2306
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1] // 2)
2307
                    attn_dbias[..., idx, :].copy_(dbias_[..., 0, :])
2308
2309
                    attn_dbias[..., (2 * cp_size - idx - 1), :].copy_(dbias_[..., 1, :])
                elif i >= (cp_size - rank - 1):
2310
2311
2312
2313
                    # [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)]
2314
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1] // 2)
2315
                    attn_dbias_[..., 1, :, idx, :].copy_(dbias_[..., 0, :])
2316
                    attn_dbias_[..., 1, :, (2 * cp_size - idx - 1), :].copy_(dbias_[..., 1, :])
2317

2318
2319
2320
            # wait until dKV is received
            for req in send_recv_reqs:
                req.wait()
2321

2322
2323
2324
2325
2326
2327
2328
            if ctx.fp8:
                if i < cp_size - 1:
                    dkv = dkv_fp8_[(rank + i + 1) % cp_size]
                else:
                    dkv = dkv_fp8[(rank + i + 1) % cp_size]
            else:
                dkv = p2p_comm_buffers[(i + 1) % 2][1]
2329
            if ctx.use_fused_attention:
2330
                if ctx.qkv_format in ["bshd", "sbhd"]:
2331
2332
2333
2334
2335
2336
2337
2338
2339
2340
2341
2342
2343
2344
                    dkv_ = _combine_tensors([dk_, dv_], -2)
                elif ctx.qkv_format == "thd":
                    dkv_ = torch.cat(
                        (dk_.unsqueeze(0), dv_.unsqueeze(0)), dim=0
                    )  # pylint: disable=used-before-assignment
            if ctx.qkv_format in ["bshd", "sbhd"]:
                # [b, 2, sk//2, 2, np, hn] -> [2, b, 2, sk//2, np, hn] or
                # [2, sk//2, b, 2, np, hn] -> [2, 2, sk//2, b, np, hn]
                dkv = dkv.view(2, *dkv.shape[0:-3], *dkv.shape[-2:])
                dkv_ = dkv_.movedim(-3, 0)
                if causal and (i < (cp_size - rank - 1) or i == (cp_size - 1)):
                    # [2, b, sk, np, hn] -> [2, b, 2, sk//2, np, hn] or
                    # [2, sk, b, np, hn] -> [2, 2, sk//2, b, np, hn]
                    dkv_ = dkv_.view(*dkv.shape)
2345

2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
            if ctx.fp8:
                if causal and i >= (cp_size - rank - 1) and i != (cp_size - 1):
                    if ctx.qkv_format == "bshd":
                        dkv[:, :, 0, ...].copy_(dkv_)
                        dkv[:, :, 1, ...].fill_(0)
                    elif ctx.qkv_format == "sbhd":
                        dkv[:, 0, ...].copy_(dkv_)
                        dkv[:, 1, ...].fill_(0)
                else:
                    dkv.copy_(dkv_)
            elif causal:
2357
                if i == (cp_size - 1):
2358
                    if rank == 0:
2359
2360
2361
2362
2363
2364
                        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, ...])
2365
                        elif ctx.qkv_format == "thd":
2366
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "add", "copy")
2367
2368
                    else:
                        dkv.add_(dkv_)
2369
2370
                elif i >= (cp_size - rank - 1):
                    if i == 0 and rank == (cp_size - 1):
2371
2372
2373
2374
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].copy_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].copy_(dkv_)
2375
                        elif ctx.qkv_format == "thd":
2376
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "copy", "none")
2377
                    else:
2378
2379
2380
2381
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].add_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].add_(dkv_)
2382
                        elif ctx.qkv_format == "thd":
2383
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "add", "none")
2384
2385
2386
2387
2388
                elif i > 0:
                    dkv.add_(dkv_)
                else:
                    dkv.copy_(dkv_)
            else:
2389
2390
2391
2392
2393
                if i == 0:
                    dkv.copy_(dkv_)
                else:
                    dkv.add_(dkv_)

2394
        if ctx.fp8 and ctx.use_fused_attention:
2395
            amax_cp_bwd = amax_per_step.amax(dim=1)
2396
2397
            ctx.dP_quantizer.amax.copy_(amax_cp_bwd[0])
            ctx.dQKV_CP_quantizer.amax.copy_(amax_cp_bwd[1])
2398
2399
2400
2401
            if ctx.qkv_format in ["bshd", "sbhd"]:
                # [cp, b, 2, sk//2, 2, np, hn] -> [cp, 2, b, 2, sk//2, np, hn] or
                # [cp, 2, sk//2, b, 2, np, hn] -> [cp, 2, 2, sk//2, b, np, hn]
                dkv_fp8 = dkv_fp8.view(cp_size, 2, *dkv_fp8.shape[1:-3], *dkv_fp8.shape[-2:])
2402
2403
2404
2405
2406
2407
2408
            dq = ctx.dQKV_CP_quantizer.create_tensor_from_data(
                dq_fp8, fake_dtype=torch.float32, internal=True
            )
            dkv = ctx.dQKV_CP_quantizer.create_tensor_from_data(
                dkv_fp8, fake_dtype=torch.float32, internal=True
            )
            dq, dkv = [x.dequantize(dtype=torch.float32) for x in [dq, dkv]]
2409
2410
            dq, dkv = [x.sum(dim=0).to(dout_dtype) for x in [dq, dkv]]

2411
        if causal:
2412
2413
            if ctx.qkv_format == "bshd":
                # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
2414
                dq = dq.view(dq.shape[0], -1, *dq.shape[-2:])
2415
                # [2, b, 2, sk//2, np, hn] -> [2, b, sk, np, hn]
2416
                dkv = dkv.view(*dkv.shape[0:2], -1, *dkv.shape[-2:])
2417
2418
            elif ctx.qkv_format == "sbhd":
                # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
2419
                dq = dq.view(-1, *dq.shape[-3:])
2420
                # [2, 2, sk//2, b, np, hn] -> [2, sk, b, np, hn]
2421
2422
                dkv = dkv.view(dkv.shape[0], -1, *dkv.shape[-3:])

2423
2424
2425
        if ctx.qkv_format == "thd" and not ctx.use_fused_attention:
            dq[cu_seqlens_q_padded[-1] :].fill_(0)
            dkv[:, cu_seqlens_kv_padded[-1] :].fill_(0)
2426

2427
        if ctx.fp8 and ctx.is_input_fp8:
2428
2429
            assert torch.uint8 not in [dq.dtype, dkv.dtype]
            dq, dkv = [ctx.dQKV_quantizer(x)._data for x in [dq, dkv]]
2430
2431
2432
        dk, dv = dkv[0], dkv[1]

        if cp_size_a2a > 1:
2433
            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_after_attn(cp_size_a2a, q.device)
2434
2435
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
            dq, dk, dv = flash_attn_a2a_communicate(
                [dq, dk, dv],
                chunk_ids_for_a2a,
                seq_dim,
                cp_size_a2a,
                ctx.cp_group_a2a,
                ctx.cp_stream,
                False,
            )
            if ctx.qkv_format == "bshd":
                dq, dk, dv = [x.view(ctx.batch_size, -1, *x.shape[-2:]) for x in [dq, dk, dv]]
            elif ctx.qkv_format == "sbhd":
                dq, dk, dv = [x.view(-1, ctx.batch_size, *x.shape[-2:]) for x in [dq, dk, dv]]

2448
2449
2450
        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)
2451
2452
        # converting torch.uint8 to float8tensor
        if ctx.fp8 and ctx.is_input_fp8:
2453
2454
2455
            dq = ctx.dQKV_quantizer.create_tensor_from_data(dq, fake_dtype=dout_dtype)
            dk = ctx.dQKV_quantizer.create_tensor_from_data(dk, fake_dtype=dout_dtype)
            dv = ctx.dQKV_quantizer.create_tensor_from_data(dv, fake_dtype=dout_dtype)
2456
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndKVP2P.backward")
2457

2458
2459
2460
        return (
            None,
            dq,
2461
2462
            dk,
            dv,
2463
2464
2465
2466
2467
2468
2469
2470
2471
2472
2473
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
2474
            attn_dbias,
2475
2476
2477
2478
2479
            None,
            None,
            None,
            None,
            None,
2480
2481
            None,
            None,
2482
            None,
2483
            None,
2484
            None,
2485
        )
2486
2487


2488
2489
def get_kv_seq_info_after_all_gather(
    local_chunk_id, cp_size, max_seqlen_q, max_seqlen_kv, window_size, causal
2490
):
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
2501
2502
2503
2504
2505
2506
2507
2508
2509
2510
2511
2512
    """Compute KV sequence index range and update window size after all-gather."""
    local_chunk_end_idx = (local_chunk_id + 1) * max_seqlen_kv
    full_seq_end_idx = max_seqlen_kv * cp_size * 2

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

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

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

    return (seq_start_idx, seq_end_idx), (window_size_left, window_size_right)
2513
2514
2515
2516


class AttnFuncWithCPAndKVAllGather(torch.autograd.Function):
    """
2517
2518
    Attention implementation with context parallelism. KV all-gather between CP ranks is exposed.
    Refer section 3.3.2 of `The Llama 3 Herd of Models <https://arxiv.org/abs/2407.21783>`_.
2519
2520
2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
2535
2536
2537
2538
2539
2540
    """

    @staticmethod
    def forward(
        ctx,
        is_training,
        q,
        k,
        v,
        cu_seqlens_q,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q_padded,
        dropout_p,
        softmax_scale,
        qkv_format,
        attn_mask_type,
        attn_bias_type,
        attn_bias,
        deterministic,
        use_fused_attention,
        window_size,
2541
2542
        cp_group,
        cp_stream,
2543
        use_flash_attn_3,
2544
    ):
2545
        # pylint: disable=missing-function-docstring
2546
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndKVAllGather.forward")
2547
2548
2549
2550
2551
2552
        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)

2553
2554
        qkv_dtype = q.dtype

2555
2556
        causal = "causal" in attn_mask_type
        padding = "padding" in attn_mask_type
2557
        assert not padding, f"{attn_mask_type} mask type is not supported!"
2558
2559
2560
2561
2562
        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 (
2563
            use_fused_attention or fa_utils.v2_3_plus
2564
        ), "Sliding window attention only can work with FusedAttention or FlashAttention >= 2.3!"
2565

2566
        flash_attn_fwd = None
2567
2568
        if not use_fused_attention:
            fa_forward_kwargs = {"softmax_scale": softmax_scale}
2569
2570
            if use_flash_attn_3:
                flash_attn_fwd = _flash_attn_fwd_v3
2571
            else:
2572
2573
2574
2575
                if qkv_format == "thd":
                    flash_attn_fwd = _flash_attn_varlen_fwd
                else:
                    flash_attn_fwd = _flash_attn_fwd
2576
2577
                fa_forward_kwargs["dropout_p"] = dropout_p
                fa_forward_kwargs["return_softmax"] = False
2578
                if fa_utils.v2_4_plus:
2579
                    fa_forward_kwargs["alibi_slopes"] = None
2580
                if fa_utils.v2_5_7_plus and qkv_format == "thd":
2581
                    fa_forward_kwargs["block_table"] = None
2582
                if fa_utils.v2_6_0_plus:
2583
                    fa_forward_kwargs["softcap"] = 0.0
2584
2585
2586
2587
2588
2589
2590
2591
2592
2593
2594

        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)
2595
2596
        if use_fused_attention or qkv_format == "thd":
            cu_seqlens_q = cu_seqlens_q // (2 * cp_size)
2597
2598
2599
2600
        if cu_seqlens_q_padded is not None and qkv_format == "thd":
            cu_seqlens_q_padded = cu_seqlens_q_padded // (2 * cp_size)
        else:
            cu_seqlens_q_padded = None
2601

2602
2603
2604
2605
        # [b, s, np, hn] -> [b, 2, s//2, np, hn] or [s, b, np, hn] -> [2, s//2, b, np, hn]
        q = q.view(*q.shape[:seq_dim], 2, q.shape[seq_dim] // 2, *q.shape[(seq_dim + 1) :])
        # [b, s, np, hn] or [s, b, np, hn] -> [s, b, np, hn]
        k, v = [x.movedim(seq_dim, 0).contiguous() for x in [k, v]]
2606

2607
        # [s, b, np, hn] -> [cp, s, b, np, hn]
2608
2609
        k_ag, _ = gather_along_first_dim(k, cp_group)
        v_ag, _ = gather_along_first_dim(v, cp_group)
2610
2611

        # [cp, s, b, np, hn] -> [cp*2, s//2, b, np, hn]
2612
2613
        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:])
2614
        chunk_ids_for_kv_ag = get_seq_chunk_ids_for_reordering_before_attn(cp_size, k.device)
2615
2616
2617
2618
2619
2620
2621
2622
2623
        k_ag = torch.index_select(k_ag, dim=0, index=chunk_ids_for_kv_ag)
        v_ag = torch.index_select(v_ag, dim=0, index=chunk_ids_for_kv_ag)
        # [cp*2, s//2, b, np, hn] -> [cp*s, b, np, hn]
        k_ag = k_ag.view(-1, *k.shape[1:])
        v_ag = v_ag.view(-1, *v.shape[1:])
        cp_stream.wait_stream(torch.cuda.current_stream())

        # create two streams to resolve wave quantization issue of Flash Attn in each step
        flash_attn_streams = [torch.cuda.current_stream(), cp_stream]
2624
2625

        local_seq_chunk_ids = [rank, 2 * cp_size - rank - 1]
2626
2627
2628
        kv_seq_range_per_step = [None, None]
        window_size_per_step = [None, None]
        cu_seqlens_kv_per_step = [None, None]
2629
2630
2631
2632
2633
2634
2635
2636
        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]):
2637
2638
                    # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                    # or [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
2639
2640
2641
2642
2643
2644
2645
2646
2647
                    q_ = q.select(seq_dim, i).contiguous()
                    kv_seq_range_per_step[i], window_size_per_step[i] = (
                        get_kv_seq_info_after_all_gather(
                            local_seq_chunk_ids[i],
                            cp_size,
                            max_seqlen_q,
                            max_seqlen_kv,
                            window_size,
                            causal,
2648
                        )
2649
2650
2651
2652
2653
2654
                    )
                    seq_start_idx, seq_end_idx = (
                        kv_seq_range_per_step[i][0],
                        kv_seq_range_per_step[i][1],
                    )
                    max_seqlen_kv_ = seq_end_idx - seq_start_idx
2655
                    if use_fused_attention or qkv_format == "thd":
2656
                        cu_seqlens_kv_per_step[i] = dpa_utils.get_full_cu_seqlens(
2657
2658
                            k.shape[1], max_seqlen_kv_, k.device
                        )
2659
2660
2661
                    k_, v_ = [x[seq_start_idx:seq_end_idx] for x in [k_ag, v_ag]]
                    # [s_range, b, np, hn] -> [b, s_range, np, hn] or [s_range, b, np, hn]
                    k_, v_ = [x.movedim(0, seq_dim).contiguous() for x in [k_, v_]]
2662
2663
2664
2665
                    if use_fused_attention:
                        out_per_step[i], [softmax_lse_per_step[i], rng_states[i]] = fused_attn_fwd(
                            is_training,
                            max_seqlen_q,
2666
                            max_seqlen_kv_,
2667
                            cu_seqlens_q,
2668
                            cu_seqlens_kv_per_step[i],
2669
2670
2671
                            q_,
                            k_,
                            v_,
2672
                            qkv_dtype,
2673
2674
2675
2676
2677
2678
2679
2680
                            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,
2681
2682
                            cu_seqlens_kv_padded=cu_seqlens_kv_per_step[i],
                            window_size=window_size_per_step[i],
2683
2684
                        )
                    else:
2685
2686
2687
2688
2689
2690
2691
2692
2693
2694
                        fa_forward_args_thd = get_fa_args(
                            True,
                            use_flash_attn_3,
                            qkv_format,
                            cu_seqlens_q=cu_seqlens_q,
                            cu_seqlens_kv=cu_seqlens_kv_per_step[i],
                            max_seqlen_q=max_seqlen_q,
                            max_seqlen_kv=max_seqlen_kv_,
                        )
                        if use_flash_attn_3 or (fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus):
2695
                            fa_forward_kwargs["window_size"] = window_size_per_step[i]
2696
                        elif fa_utils.v2_7_0_plus:
2697
2698
                            fa_forward_kwargs["window_size_left"] = window_size_per_step[i][0]
                            fa_forward_kwargs["window_size_right"] = window_size_per_step[i][1]
2699
2700
2701
2702
                        fa_outputs = flash_attn_fwd(
                            q_,
                            k_,
                            v_,
2703
                            *fa_forward_args_thd,
2704
2705
                            causal=causal,
                            **fa_forward_kwargs,
2706
                        )
2707
                        if not fa_utils.v2_7_0_plus:
2708
2709
                            out_per_step[i] = fa_outputs[4]
                            softmax_lse_per_step[i] = fa_outputs[5]
2710
                            if not use_flash_attn_3:
2711
2712
2713
2714
                                rng_states[i] = fa_outputs[7]
                        else:
                            out_per_step[i] = fa_outputs[0]
                            softmax_lse_per_step[i] = fa_outputs[1]
2715
                            if not use_flash_attn_3:
2716
                                rng_states[i] = fa_outputs[3]
2717
2718
2719
2720

            if i > 0:
                with torch.cuda.stream(flash_attn_streams[i - 1]):
                    if qkv_format == "bshd":
2721
                        out[:, i - 1].copy_(out_per_step[i - 1])
2722
                    elif qkv_format == "sbhd":
2723
                        out[i - 1].copy_(out_per_step[i - 1])
2724
2725
2726
2727
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740

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

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

        ctx.save_for_backward(
            q,
            k,
            v,
            cu_seqlens_q,
            cu_seqlens_q_padded,
2741
            *cu_seqlens_kv_per_step,
2742
2743
2744
2745
            *out_per_step,
            *softmax_lse_per_step,
            *rng_states,
        )
2746
2747

        ctx.qkv_dtype = qkv_dtype
2748
2749
        ctx.kv_seq_range_per_step = kv_seq_range_per_step
        ctx.window_size_per_step = window_size_per_step
2750
2751
2752
2753
2754
2755
2756
        ctx.cp_group = cp_group
        ctx.cp_stream = cp_stream
        ctx.dropout_p = dropout_p
        ctx.max_seqlen_q = max_seqlen_q
        ctx.softmax_scale = softmax_scale
        ctx.qkv_format = qkv_format
        ctx.attn_bias_type = attn_bias_type
2757
        ctx.attn_mask_type = attn_mask_type
2758
2759
        ctx.deterministic = deterministic
        ctx.use_fused_attention = use_fused_attention
2760
        ctx.use_flash_attn_3 = use_flash_attn_3
2761
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndKVAllGather.forward")
2762
2763
2764
2765
        return out

    @staticmethod
    def backward(ctx, dout):
2766
        # pylint: disable=missing-function-docstring
2767
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndKVAllGather.backward")
2768
2769
2770
        cp_size = get_distributed_world_size(ctx.cp_group)
        rank = get_distributed_rank(ctx.cp_group)

2771
2772
2773
2774
2775
2776
        (*saved_tensors,) = ctx.saved_tensors
        (q, k, v, cu_seqlens_q, cu_seqlens_q_padded) = saved_tensors[:5]
        cu_seqlens_kv_per_step = saved_tensors[5:7]
        out_per_step = saved_tensors[7:9]
        softmax_lse_per_step = saved_tensors[9:11]
        rng_states = saved_tensors[11:13]
2777
2778
        kv_seq_range_per_step = ctx.kv_seq_range_per_step
        window_size_per_step = ctx.window_size_per_step
2779

2780
        seq_dim = ctx.qkv_format.index("s")
2781
2782
        qkv_layout = ctx.qkv_format + "_" + ctx.qkv_format + "_" + ctx.qkv_format

2783
        dout = dout.view(q.shape)
2784
        dq = torch.empty_like(q)
2785
        dk = torch.zeros((k.shape[0] * cp_size, *k.shape[1:]), dtype=k.dtype, device=k.device)
2786
2787
2788
2789
2790
2791
2792
2793
2794
2795
        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()

2796
        # [s, b, np, hn] -> [cp, s, b, np, hn]
2797
2798
        k_ag, _ = gather_along_first_dim(k, ctx.cp_group)
        v_ag, _ = gather_along_first_dim(v, ctx.cp_group)
2799
2800

        # [cp, s, b, np, hn] -> [cp*2, s//2, b, np, hn]
2801
2802
        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:])
2803
        chunk_ids_for_kv_ag = get_seq_chunk_ids_for_reordering_before_attn(cp_size, k.device)
2804
2805
2806
2807
2808
2809
        k_ag = torch.index_select(k_ag, dim=0, index=chunk_ids_for_kv_ag)
        v_ag = torch.index_select(v_ag, dim=0, index=chunk_ids_for_kv_ag)
        # [cp*2, s//2, b, np, hn] -> [cp*s, b, np, hn]
        k_ag = k_ag.view(-1, *k.shape[1:])
        v_ag = v_ag.view(-1, *v.shape[1:])
        ctx.cp_stream.wait_stream(torch.cuda.current_stream())
2810
2811
2812

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

2813
        flash_attn_bwd = None
2814
2815
        if not ctx.use_fused_attention:
            fa_backward_kwargs = {"softmax_scale": ctx.softmax_scale}
2816
2817
            if ctx.use_flash_attn_3:
                flash_attn_bwd = _flash_attn_bwd_v3
2818
2819
                fa_backward_kwargs["deterministic"] = ctx.deterministic
            else:
2820
2821
2822
2823
                if ctx.qkv_format == "thd":
                    flash_attn_bwd = _flash_attn_varlen_bwd
                else:
                    flash_attn_bwd = _flash_attn_bwd
2824
                fa_backward_kwargs["dropout_p"] = ctx.dropout_p
2825
                if fa_utils.v2_4_plus:
2826
                    fa_backward_kwargs["alibi_slopes"] = None
2827
                if fa_utils.v2_4_1_plus:
2828
                    fa_backward_kwargs["deterministic"] = ctx.deterministic
2829
                if fa_utils.v2_6_0_plus:
2830
                    fa_backward_kwargs["softcap"] = 0.0
2831
2832
2833
2834

        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]):
2835
2836
                    # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                    # or [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
2837
2838
2839
2840
2841
2842
2843
2844
2845
                    q_ = q.select(seq_dim, i).contiguous()
                    seq_start_idx, seq_end_idx = (
                        kv_seq_range_per_step[i][0],
                        kv_seq_range_per_step[i][1],
                    )
                    max_seqlen_kv = seq_end_idx - seq_start_idx
                    k_, v_ = [x[seq_start_idx:seq_end_idx] for x in [k_ag, v_ag]]
                    # [cp*s, b, np, hn] -> [b, s_range, np, hn] or [s_range, b, np, hn]
                    k_, v_ = [x.movedim(0, seq_dim).contiguous() for x in [k_, v_]]
2846
                    out_ = out_per_step[i]
2847
                    dout_ = dout.select(seq_dim, i).contiguous().view(out_.shape)
2848
2849
2850
2851
                    if ctx.use_fused_attention:
                        aux_ctx_tensors = [softmax_lse_per_step[i], rng_states[i]]
                        dq_per_step[i], dk_per_step[i], dv_per_step[i], _ = fused_attn_bwd(
                            ctx.max_seqlen_q,
2852
                            max_seqlen_kv,
2853
                            cu_seqlens_q,
2854
                            cu_seqlens_kv_per_step[i],
2855
2856
2857
2858
2859
                            q_,
                            k_,
                            v_,
                            out_,
                            dout_,
2860
                            ctx.qkv_dtype,
2861
                            TE_DType[dout.dtype],
2862
2863
2864
                            aux_ctx_tensors,
                            tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
2865
                            cu_seqlens_kv_padded=cu_seqlens_kv_per_step[i],
2866
2867
2868
2869
2870
                            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,
2871
2872
                            window_size=window_size_per_step[i],
                            deterministic=ctx.deterministic,
2873
2874
2875
2876
2877
                        )
                    else:
                        dq_per_step[i], dk_per_step[i], dv_per_step[i] = [
                            torch.empty_like(x) for x in [q_, k_, v_]
                        ]
2878
2879
2880
2881
2882
2883
2884
2885
2886
2887
2888
2889
2890
                        fa_backward_args_thd = get_fa_args(
                            False,
                            ctx.use_flash_attn_3,
                            ctx.qkv_format,
                            cu_seqlens_q=cu_seqlens_q,
                            cu_seqlens_kv=cu_seqlens_kv_per_step[i],
                            max_seqlen_q=ctx.max_seqlen_q,
                            max_seqlen_kv=max_seqlen_kv,
                            dq=dq_per_step[i],
                            dk=dk_per_step[i],
                            dv=dv_per_step[i],
                        )
                        if not ctx.use_flash_attn_3:
2891
                            fa_backward_kwargs["rng_state"] = rng_states[i]
2892
2893
2894
                        if ctx.use_flash_attn_3 or (
                            fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus
                        ):
2895
                            fa_backward_kwargs["window_size"] = window_size_per_step[i]
2896
                        elif fa_utils.v2_7_0_plus:
2897
2898
                            fa_backward_kwargs["window_size_left"] = window_size_per_step[i][0]
                            fa_backward_kwargs["window_size_right"] = window_size_per_step[i][1]
2899
                        flash_attn_bwd(
2900
2901
2902
2903
2904
2905
                            dout_,
                            q_,
                            k_,
                            v_,
                            out_,
                            softmax_lse_per_step[i],
2906
                            *fa_backward_args_thd,
2907
2908
                            causal="causal" in ctx.attn_mask_type,
                            **fa_backward_kwargs,
2909
2910
2911
2912
2913
                        )

            if i > 0:
                with torch.cuda.stream(flash_attn_streams[i - 1]):
                    if ctx.qkv_format == "bshd":
2914
                        dq[:, i - 1].copy_(dq_per_step[i - 1])
2915
                    elif ctx.qkv_format == "sbhd":
2916
2917
2918
2919
2920
2921
                        dq[i - 1].copy_(dq_per_step[i - 1])
                    # [b, s_range, np, hn] or [s_range, b, np, hn] -> [s_range, b, np, hn]
                    dk_per_step[i - 1], dv_per_step[i - 1] = [
                        x.movedim(seq_dim, 0).contiguous()
                        for x in [dk_per_step[i - 1], dv_per_step[i - 1]]
                    ]
2922
2923
2924
                    # wait until dkv update of last step is done
                    if i > 1:
                        flash_attn_streams[i - 1].wait_event(dkv_update_done)
2925
2926
2927
2928
2929
2930
                    seq_start_idx, seq_end_idx = (
                        kv_seq_range_per_step[i - 1][0],
                        kv_seq_range_per_step[i - 1][1],
                    )
                    dk[seq_start_idx:seq_end_idx].add_(dk_per_step[i - 1])
                    dv[seq_start_idx:seq_end_idx].add_(dv_per_step[i - 1])
2931
2932
2933
2934
2935
                    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)

2936
2937
2938
        # [cp*s, b, np, hn] -> [cp*2, s//2, b, np, hn]
        dk = dk.view(2 * cp_size, -1, *dk.shape[-3:])
        dv = dv.view(2 * cp_size, -1, *dv.shape[-3:])
2939
        chunk_ids_for_kv_ag = get_seq_chunk_ids_for_reordering_after_attn(cp_size, dk.device)
2940
2941
2942
        dk = torch.index_select(dk, dim=0, index=chunk_ids_for_kv_ag)
        dv = torch.index_select(dv, dim=0, index=chunk_ids_for_kv_ag)
        # [cp*2, s//2, b, np, hn] -> [cp*s, b, np, hn]
2943
2944
2945
2946
2947
        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)

2948
2949
2950
        dq = dq.view(*dq.shape[:seq_dim], -1, *dq.shape[(seq_dim + 2) :])
        dk = dk.movedim(0, seq_dim).contiguous()
        dv = dv.movedim(0, seq_dim).contiguous()
2951
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndKVAllGather.backward")
2952
2953
2954
2955
2956
2957
2958
2959
2960
2961
2962
2963
2964
2965
2966
2967
2968
2969
2970
2971
2972

        return (
            None,
            dq,
            dk,
            dv,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
2973
            None,
2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
2990
2991
2992
2993
2994
2995
2996
2997
2998
2999
3000
3001
3002
3003
3004
3005
3006
3007
3008
        )


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

    @staticmethod
    def forward(
        ctx,
        is_training,
        q,
        k,
        v,
        cu_seqlens_q,
        cu_seqlens_kv,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
        dropout_p,
        softmax_scale,
        qkv_format,
        attn_mask_type,
        attn_bias_type,
        attn_bias,
        deterministic,
        use_fused_attention,
        window_size,
        fp8,
        fp8_meta,
        cp_group,
        cp_stream,
3009
        quantizers,
3010
        use_flash_attn_3,
3011
    ):
3012
        # pylint: disable=missing-function-docstring
3013
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndQKVOA2A.forward")
3014
3015
3016
3017
        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)

        cp_size = get_distributed_world_size(cp_group)
3018
        qkv_dtype = q.dtype
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028

        causal = "causal" in attn_mask_type
        padding = "padding" in attn_mask_type
        assert not padding, f"{attn_mask_type} mask type is not supported!"
        assert attn_bias_type == "no_bias", f"{attn_bias_type} bias type is not supported!"
        assert q.shape[-1] % 8 == 0, "Hidden size per attention head should be multiple of 8!"
        assert (
            window_size == (-1, 0)
            or window_size == (-1, -1)
            or use_fused_attention
3029
            or fa_utils.v2_3_plus
3030
        ), "Sliding window attention only can work with FusedAttention or FlashAttention >= 2.3!"
3031

3032
        flash_attn_fwd = None
3033
3034
        if not use_fused_attention:
            fa_forward_kwargs = {"softmax_scale": softmax_scale}
3035
3036
            if use_flash_attn_3:
                flash_attn_fwd = _flash_attn_fwd_v3
3037
3038
                fa_forward_kwargs["window_size"] = window_size
            else:
3039
3040
3041
3042
                if qkv_format == "thd":
                    flash_attn_fwd = _flash_attn_varlen_fwd
                else:
                    flash_attn_fwd = _flash_attn_fwd
3043
3044
                fa_forward_kwargs["dropout_p"] = dropout_p
                fa_forward_kwargs["return_softmax"] = False
3045
                if fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus:
3046
                    fa_forward_kwargs["window_size"] = window_size
3047
                elif fa_utils.v2_7_0_plus:
3048
3049
                    fa_forward_kwargs["window_size_left"] = window_size[0]
                    fa_forward_kwargs["window_size_right"] = window_size[1]
3050
                if fa_utils.v2_4_plus:
3051
                    fa_forward_kwargs["alibi_slopes"] = None
3052
                if fa_utils.v2_5_7_plus and qkv_format == "thd":
3053
                    fa_forward_kwargs["block_table"] = None
3054
                if fa_utils.v2_6_0_plus:
3055
                    fa_forward_kwargs["softcap"] = 0.0
3056
3057
3058
3059
3060
3061
3062
3063
3064
3065
3066
3067
3068
3069

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

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

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

3070
        fused_attn_backend = None
3071
3072
        # "fp8_mha" decides outputs in fp8, while inputs are inferred from the real dtype
        is_input_fp8 = False
3073
3074
3075
        is_output_fp8 = False

        QKV_quantizer, O_quantizer, S_quantizer, dQKV_quantizer, dO_quantizer, dP_quantizer = (
3076
            dpa_utils.get_attention_quantizers(fp8, quantizers, cp_specific_quantizers=False)
3077
3078
3079
        )
        if fp8:
            if use_fused_attention:
3080
                fused_attn_backend = FusedAttnBackend["FP8"]
3081
3082
3083
3084
                assert isinstance(k, q.__class__) and isinstance(
                    v, q.__class__
                ), "q, k, and v must have the same type."
                is_input_fp8 = isinstance(q, Float8Tensor)
3085
                is_output_fp8 = fp8_meta is not None and fp8_meta["recipe"].fp8_mha
3086
                if is_input_fp8:
3087
                    QKV_quantizer = q._quantizer
3088
3089
3090
3091
                    q_fp8, k_fp8, v_fp8 = q, k, v
                    q, k, v = q_fp8._data, k_fp8._data, v_fp8._data
                elif int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                    q_f16, k_f16, v_f16 = q, k, v
3092
                    q, k, v = [QKV_quantizer(x)._data for x in [q_f16, k_f16, v_f16]]
3093
                fp8_meta_kwargs = {}
3094
3095
                fp8_meta_kwargs["s_quantizer"] = S_quantizer
                fp8_meta_kwargs["o_quantizer"] = O_quantizer  # partial result quantizer
3096
3097
3098
3099
3100
3101
3102
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
            if use_fused_attention:
                fp8_meta_kwargs = {}
                fused_attn_backend = FusedAttnBackend["F16_arbitrary_seqlen"]

3103
        chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_before_attn(cp_size, q.device)
3104
3105
3106
3107
        q, k, v = flash_attn_a2a_communicate(
            [q, k, v], chunk_ids_for_a2a, seq_dim, cp_size, cp_group, cp_stream, True
        )

3108
        if fp8 and not is_input_fp8 and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
3109
            q_f16, k_f16, v_f16 = q, k, v
3110
            q, k, v = [QKV_quantizer(x)._data for x in [q_f16, k_f16, v_f16]]
3111
3112
3113

        batch_size = q.shape[batch_dim]
        if use_fused_attention:
3114
3115
3116
3117
3118
3119
3120
3121
3122
3123
3124
            q_part, k_part, v_part = q, k, v
            if fp8:
                q_part = QKV_quantizer.create_tensor_from_data(
                    q, fake_dtype=qkv_dtype, internal=True
                )
                k_part = QKV_quantizer.create_tensor_from_data(
                    k, fake_dtype=qkv_dtype, internal=True
                )
                v_part = QKV_quantizer.create_tensor_from_data(
                    v, fake_dtype=qkv_dtype, internal=True
                )
3125
3126
3127
3128
3129
3130
            out, aux_ctx_tensors = fused_attn_fwd(
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
3131
3132
3133
3134
                q_part,
                k_part,
                v_part,
                qkv_dtype,
3135
3136
3137
3138
3139
3140
3141
3142
3143
3144
3145
3146
                fused_attn_backend,
                attn_scale=softmax_scale,
                dropout=dropout_p,
                qkv_layout=qkv_layout,
                attn_mask_type=attn_mask_type,
                attn_bias_type=attn_bias_type,
                attn_bias=attn_bias,
                cu_seqlens_q_padded=cu_seqlens_q_padded,
                cu_seqlens_kv_padded=cu_seqlens_kv_padded,
                window_size=window_size,
                **fp8_meta_kwargs,
            )
3147
3148
            if fp8:
                out = out._data
3149
        else:
3150
3151
3152
3153
3154
3155
3156
3157
3158
            fa_forward_args_thd = get_fa_args(
                True,
                use_flash_attn_3,
                qkv_format,
                cu_seqlens_q=cu_seqlens_q,
                cu_seqlens_kv=cu_seqlens_kv,
                max_seqlen_q=max_seqlen_q,
                max_seqlen_kv=max_seqlen_kv,
            )
3159
            fa_outputs = flash_attn_fwd(
3160
3161
3162
                q,
                k,
                v,
3163
                *fa_forward_args_thd,
3164
                causal=causal,
3165
                **fa_forward_kwargs,
3166
            )
3167
            if not fa_utils.v2_7_0_plus:
3168
                out, softmax_lse = fa_outputs[4], fa_outputs[5]
3169
                rng_state = fa_outputs[7] if not use_flash_attn_3 else None
3170
3171
            else:
                out, softmax_lse = fa_outputs[0], fa_outputs[1]
3172
                rng_state = fa_outputs[3] if not use_flash_attn_3 else None
3173
3174
            aux_ctx_tensors = [softmax_lse, rng_state]

3175
        chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_after_attn(cp_size, out.device)
3176
3177
3178
3179
3180
3181
3182
3183
3184
3185
3186
3187
3188
        out = flash_attn_a2a_communicate(
            out, chunk_ids_for_a2a, seq_dim, cp_size, cp_group, cp_stream, False
        )

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

        if fp8:
3189
            if is_output_fp8:
3190
3191
                out_fp8 = O_quantizer.create_tensor_from_data(
                    out, fake_dtype=qkv_dtype, internal=False
3192
3193
                )
                out_ret = out_fp8
3194
                out = out_fp8._data
3195
            else:
3196
                out_fp8 = O_quantizer.create_tensor_from_data(
3197
                    out, fake_dtype=qkv_dtype, internal=True
3198
                )
3199
                out_f16 = out_fp8.dequantize(dtype=qkv_dtype)
3200
3201
3202
3203
                out_ret = out_f16
        else:
            out_ret = out

3204
        if not fp8 or int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
3205
            q_save, k_save, v_save, out_save = q, k, v, out
3206
3207
3208
3209
3210
3211
3212
3213
3214
        else:
            if is_input_fp8:
                q_save, k_save, v_save = q, k, v
            else:
                q_save, k_save, v_save = q_f16, k_f16, v_f16
            if is_output_fp8:
                out_save = out
            else:
                out_save = out_f16
3215

3216
        tensors_to_save, tensor_objects = prepare_for_saving(
3217
3218
3219
3220
3221
3222
3223
3224
3225
3226
            q_save,
            k_save,
            v_save,
            out_save,
            cu_seqlens_q,
            cu_seqlens_kv,
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
            *aux_ctx_tensors,
        )
3227
3228
3229
        ctx.save_for_backward(*tensors_to_save)
        ctx.tensor_objects = tensor_objects

3230
3231
3232
3233
3234
3235
3236
3237
3238
3239
3240
3241
3242
3243
3244
        ctx.batch_size = batch_size
        ctx.cp_group = cp_group
        ctx.cp_stream = cp_stream
        ctx.dropout_p = dropout_p
        ctx.max_seqlen_q = max_seqlen_q
        ctx.max_seqlen_kv = max_seqlen_kv
        ctx.softmax_scale = softmax_scale
        ctx.qkv_format = qkv_format
        ctx.attn_mask_type = attn_mask_type
        ctx.attn_bias_type = attn_bias_type
        ctx.deterministic = deterministic
        ctx.window_size = window_size
        ctx.use_fused_attention = use_fused_attention
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        ctx.fp8_meta = fp8_meta
3245
3246
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
3247
        ctx.use_flash_attn_3 = use_flash_attn_3
3248
3249
3250
3251
3252
3253
3254
3255
3256
3257
3258
3259
3260
3261
3262

        ctx.qkv_dtype = qkv_dtype
        ctx.dQKV_quantizer = dQKV_quantizer
        ctx.dO_quantizer = dO_quantizer
        ctx.dP_quantizer = dP_quantizer
        ctx.QKV_quantizer = QKV_quantizer
        ctx.O_quantizer = O_quantizer
        ctx.S_quantizer = S_quantizer
        if ctx.fp8:
            ctx.QKV_quantizer = QKV_quantizer.copy()
            ctx.QKV_quantizer.scale = QKV_quantizer.scale.clone()
            ctx.O_quantizer = O_quantizer.copy()
            ctx.O_quantizer.scale = O_quantizer.scale.clone()
            ctx.S_quantizer = S_quantizer.copy()
            ctx.S_quantizer.scale = S_quantizer.scale.clone()
3263
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndQKVOA2A.forward")
3264
3265
3266
3267
        return out_ret

    @staticmethod
    def backward(ctx, dout):
3268
        # pylint: disable=missing-function-docstring
3269
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndQKVOA2A.backward")
3270
3271
        cp_size = get_distributed_world_size(ctx.cp_group)

3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
3282
        (
            q,
            k,
            v,
            out,
            cu_seqlens_q,
            cu_seqlens_kv,
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
            *aux_ctx_tensors,
        ) = restore_from_saved(ctx.tensor_objects, ctx.saved_tensors)
3283
3284
3285
3286
3287

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

3288
        dout_dtype = dout.dtype
3289
3290
        fused_attn_backend = None
        fused_attn_dqkv_dtype = None
3291
3292
3293
        if ctx.fp8:
            if ctx.use_fused_attention:
                fused_attn_backend = FusedAttnBackend["FP8"]
3294
                if ctx.is_output_fp8:
3295
                    assert isinstance(dout, Float8Tensor), "dout must be Float8Tensors for FP8 MHA!"
3296
                    ctx.dO_quantizer = dout._quantizer
3297
                else:
3298
                    dout = ctx.dO_quantizer(dout)
3299
                fused_attn_dqkv_dtype = TE_DType[dout._data.dtype]
3300
                dout = dout._data
3301
                fp8_meta_kwargs = {}
3302
3303
3304
3305
                fp8_meta_kwargs["s_quantizer"] = ctx.S_quantizer
                fp8_meta_kwargs["dp_quantizer"] = ctx.dP_quantizer
                fp8_meta_kwargs["dqkv_quantizer"] = ctx.dQKV_quantizer

3306
3307
3308
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
3309
3310
3311
3312
3313
3314
3315
3316
3317
3318
3319
3320
3321
3322
3323
3324
            if ctx.fp8_meta is not None:
                if ctx.is_output_fp8:
                    assert isinstance(dout, Float8Tensor), "dout must be Float8Tensors for FP8 MHA!"
                    ctx.dO_quantizer = dout._quantizer
                    dout = dout._data
                if ctx.is_input_fp8:
                    q = ctx.QKV_quantizer.create_tensor_from_data(
                        q, fake_dtype=ctx.qkv_dtype, internal=True
                    )
                    k = ctx.QKV_quantizer.create_tensor_from_data(
                        k, fake_dtype=ctx.qkv_dtype, internal=True
                    )
                    v = ctx.QKV_quantizer.create_tensor_from_data(
                        v, fake_dtype=ctx.qkv_dtype, internal=True
                    )
                    q, k, v = [x.dequantize(dtype=ctx.qkv_dtype) for x in [q, k, v]]
3325
3326
            if ctx.use_fused_attention:
                fp8_meta_kwargs = {}
3327
                fused_attn_dqkv_dtype = TE_DType[dout_dtype]
3328
3329
3330
3331
3332
3333
                fused_attn_backend = FusedAttnBackend["F16_arbitrary_seqlen"]

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

3334
        chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_before_attn(cp_size, out.device)
3335
3336
3337
        out, dout = flash_attn_a2a_communicate(
            [out, dout], chunk_ids_for_a2a, seq_dim, cp_size, ctx.cp_group, ctx.cp_stream, True
        )
3338
3339
3340
3341
3342
3343
3344
3345
3346
        if not ctx.fp8 and ctx.fp8_meta is not None and ctx.is_output_fp8:
            out = ctx.O_quantizer.create_tensor_from_data(
                out, fake_dtype=ctx.qkv_dtype, internal=True
            )
            dout = ctx.dO_quantizer.create_tensor_from_data(
                dout, fake_dtype=dout_dtype, internal=True
            )
            out = out.dequantize(dtype=ctx.qkv_dtype)
            dout = dout.dequantize(dtype=dout_dtype)
3347

3348
        flash_attn_bwd = None
3349
3350
        if not ctx.use_fused_attention:
            fa_backward_kwargs = {"softmax_scale": ctx.softmax_scale}
3351
3352
3353
3354
            if ctx.use_flash_attn_3:
                flash_attn_bwd = (
                    _flash_attn_bwd_v3  # pylint: disable=possibly-used-before-assignment
                )
3355
3356
3357
                fa_backward_kwargs["window_size"] = ctx.window_size
                fa_backward_kwargs["deterministic"] = ctx.deterministic
            else:
3358
3359
3360
3361
                if ctx.qkv_format == "thd":
                    flash_attn_bwd = _flash_attn_varlen_bwd
                else:
                    flash_attn_bwd = _flash_attn_bwd
3362
                fa_backward_kwargs["dropout_p"] = ctx.dropout_p
3363
                if fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus:
3364
                    fa_backward_kwargs["window_size"] = ctx.window_size
3365
                elif fa_utils.v2_7_0_plus:
3366
3367
                    fa_backward_kwargs["window_size_left"] = ctx.window_size[0]
                    fa_backward_kwargs["window_size_right"] = ctx.window_size[1]
3368
                if fa_utils.v2_4_plus:
3369
                    fa_backward_kwargs["alibi_slopes"] = None
3370
                if fa_utils.v2_4_1_plus:
3371
                    fa_backward_kwargs["deterministic"] = ctx.deterministic
3372
                if fa_utils.v2_6_0_plus:
3373
                    fa_backward_kwargs["softcap"] = 0.0
3374
3375

        if ctx.use_fused_attention:
3376
3377
3378
3379
3380
3381
3382
3383
3384
3385
3386
3387
3388
3389
3390
3391
3392
3393
3394
3395
            q_part = q
            k_part = k
            v_part = v
            out_part = out
            dout_part = dout

            if ctx.fp8:
                q_part = ctx.QKV_quantizer.create_tensor_from_data(
                    q_part, fake_dtype=ctx.qkv_dtype, internal=True
                )
                k_part = ctx.QKV_quantizer.create_tensor_from_data(
                    k_part, fake_dtype=ctx.qkv_dtype, internal=True
                )
                v_part = ctx.QKV_quantizer.create_tensor_from_data(
                    v_part, fake_dtype=ctx.qkv_dtype, internal=True
                )
                out_part = ctx.O_quantizer.create_tensor_from_data(
                    out_part, fake_dtype=ctx.qkv_dtype, internal=True
                )
                dout_part = ctx.dO_quantizer.create_tensor_from_data(
3396
                    dout_part, fake_dtype=dout_dtype, internal=True
3397
3398
                )

3399
3400
3401
3402
3403
            dq, dk, dv, _ = fused_attn_bwd(
                ctx.max_seqlen_q,
                ctx.max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
3404
3405
3406
3407
3408
                q_part,
                k_part,
                v_part,
                out_part,
                dout_part,
3409
                dout_dtype,
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                fused_attn_dqkv_dtype,
                aux_ctx_tensors,
                fused_attn_backend,
                cu_seqlens_q_padded=cu_seqlens_q_padded,
                cu_seqlens_kv_padded=cu_seqlens_kv_padded,
                attn_scale=ctx.softmax_scale,
                dropout=ctx.dropout_p,
                qkv_layout=qkv_layout,
                attn_mask_type=ctx.attn_mask_type,
                attn_bias_type=ctx.attn_bias_type,
                window_size=ctx.window_size,
                deterministic=ctx.deterministic,
                **fp8_meta_kwargs,
            )
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            if ctx.fp8:
                dq = dq._data
                dk = dk._data
                dv = dv._data
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        else:
            softmax_lse, rng_state = aux_ctx_tensors
            dq, dk, dv = [torch.empty_like(x) for x in [q, k, v]]
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            fa_backward_args_thd = get_fa_args(
                False,
                ctx.use_flash_attn_3,
                ctx.qkv_format,
                cu_seqlens_q=cu_seqlens_q,
                cu_seqlens_kv=cu_seqlens_kv,
                max_seqlen_q=ctx.max_seqlen_q,
                max_seqlen_kv=ctx.max_seqlen_kv,
                dq=dq,
                dk=dk,
                dv=dv,
            )
            if not ctx.use_flash_attn_3:
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                fa_backward_kwargs["rng_state"] = rng_state
            flash_attn_bwd(
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                dout,
                q,
                k,
                v,
                out,
                softmax_lse,
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                *fa_backward_args_thd,
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                causal=causal,
                **fa_backward_kwargs,
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            )

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

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

        if ctx.fp8:
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            dq = ctx.dQKV_quantizer.create_tensor_from_data(
                dq, fake_dtype=dout_dtype, internal=not ctx.is_input_fp8
            )
            dk = ctx.dQKV_quantizer.create_tensor_from_data(
                dk, fake_dtype=dout_dtype, internal=not ctx.is_input_fp8
            )
            dv = ctx.dQKV_quantizer.create_tensor_from_data(
                dv, fake_dtype=dout_dtype, internal=not ctx.is_input_fp8
            )
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            if not ctx.is_input_fp8:
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                dq, dk, dv = [x.dequantize(dtype=dout_dtype) for x in [dq, dk, dv]]
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        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndQKVOA2A.backward")
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        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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            None,
            None,
            None,
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            None,
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        )


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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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    fp8=False,
    fp8_meta=None,
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    quantizers=None,
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    pad_between_seqs=False,
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    use_flash_attn_3=False,
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) -> torch.Tensor:
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    """
    Attention implementation with context parallelism.
    """

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

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

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    if cp_comm_type in ["p2p", "a2a+p2p"]:
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        args += [
            fp8,
            fp8_meta,
            cp_group,
            cp_global_ranks,
            cp_stream,
            quantizers,
            pad_between_seqs,
            use_flash_attn_3,
        ]
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        out = AttnFuncWithCPAndKVP2P.apply(*args)
    elif cp_comm_type == "all_gather":
        args.pop(5)
        args.pop(8)
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        args += [window_size, cp_group, cp_stream, use_flash_attn_3]
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        out = AttnFuncWithCPAndKVAllGather.apply(*args)
    elif cp_comm_type == "a2a":
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        args += [window_size, fp8, fp8_meta, cp_group, cp_stream, quantizers, use_flash_attn_3]
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        out = AttnFuncWithCPAndQKVOA2A.apply(*args)
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    else:
        raise ValueError(f"Unsupported communication type: {cp_comm_type}!")

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    return out


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

    @staticmethod
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    def forward(
        ctx,
        mixed_x_layer: torch.Tensor,
        split_dim: int,
        split_size_or_sections: Union[int, List[int], Tuple[int]],
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        squeeze=False,
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    ) -> Tuple[torch.Tensor, ...]:
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        # pylint: disable=missing-function-docstring
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        ctx.split_dim = split_dim
        ctx.split_size_or_sections = split_size_or_sections
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        if isinstance(mixed_x_layer, Float8TensorBase) and not isinstance(
            mixed_x_layer, Float8Tensor
        ):
            return tuple(
                Float8TensorBase(
                    fp8_scale_inv=mixed_x_layer._scale_inv,
                    fp8_dtype=mixed_x_layer._fp8_dtype,
                    data=x.squeeze(split_dim) if squeeze else x,
                    shape=x.squeeze(split_dim).shape if squeeze else x.shape,
                    quantizer=mixed_x_layer._quantizer,
                )
                for x in torch.split(
                    mixed_x_layer._data,
                    split_size_or_sections=split_size_or_sections,
                    dim=split_dim,
                )
            )
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        if isinstance(mixed_x_layer, Float8Tensor):
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            return tuple(
                Float8Tensor.make_like(
                    mixed_x_layer,
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                    data=x.squeeze(split_dim) if squeeze else x,
                    shape=x.squeeze(split_dim).shape if squeeze else x.shape,
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                )
                for x in torch.split(
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                    mixed_x_layer._data,
                    split_size_or_sections=split_size_or_sections,
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                    dim=split_dim,
                )
            )
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        out_list = torch.split(mixed_x_layer, split_size_or_sections, dim=split_dim)
        if squeeze:
            out_list = [x.squeeze(split_dim) for x in out_list]
        return out_list
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    @staticmethod
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    def backward(ctx, *grad_outputs):
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        # pylint: disable=missing-function-docstring
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        assert len(grad_outputs) > 0, "No gradients received for backprop!"

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

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

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

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

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

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        # An FP16 training trick required for certain GPT-like models.
        self.apply_qk_layer_scaling = (
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            bool(int(os.getenv("NVTE_APPLY_QK_LAYER_SCALING", "0"))) and layer_number is not None
        )
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    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
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        qkv_layout: str = "sbh3d",
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        cu_seqlens_q: Optional[torch.Tensor] = None,  # pylint: disable=unused-argument
        cu_seqlens_kv: Optional[torch.Tensor] = None,  # pylint: disable=unused-argument
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        attn_mask_type: str = "causal",
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        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
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        window_size: Optional[Tuple[int, int]] = None,
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        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
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        alibi_slopes: Optional[torch.Tensor] = None,
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        inference_params: Optional[InferenceParams] = 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}!"
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        # get q_format and kv_format for training and inference
        qkv_format, q_format, _ = dpa_utils.get_qkv_format(qkv_layout, inference_params)
        if inference_params is not None and inference_params.is_paged:
            key_layer, value_layer = inference_params.convert_paged_to_nonpaged(self.layer_number)

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        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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        if qkv_format == "sbhd_2bshd":
            key_layer, value_layer = [x.transpose(0, 1) for x in [key_layer, value_layer]]

        total_tokens, batch_size = None, None
        if qkv_format == "thd_2bshd":
            total_tokens, batch_size = query_layer.shape[0], key_layer.shape[0]
            query_layer = tex.convert_thd_to_bshd(
                query_layer,
                cu_seqlens_q,
                batch_size,
                inference_params.max_ctx_len,
            )
            query_layer, key_layer, value_layer = [
                x.transpose(0, 1) for x in [query_layer, key_layer, value_layer]
            ]
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        batch_size, max_seqlen_q, max_seqlen_kv = (
            query_layer.shape[1],
            query_layer.shape[0],
            key_layer.shape[0],
        )
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        if "padding" in attn_mask_type and attention_mask is None:
            attention_mask = dpa_utils.get_padding_mask(
                batch_size, cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv
            )
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        attn_mask_type, attention_mask, actual_seqlens_q, actual_seqlens_kv = (
            dpa_utils.get_full_mask(
                max_seqlen_q,
                max_seqlen_kv,
                attn_mask_type=attn_mask_type,
                attention_mask=attention_mask,
                window_size=window_size,
                attention_type=self.attention_type,
            )
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        )
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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]
        matmul_result = torch.empty(
            output_size[0] * output_size[1],
            output_size[2],
            output_size[3],
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            dtype=query_layer.dtype,
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            device=torch.cuda.current_device(),
        )

3904
        scale = self.softmax_scale
3905
        if apply_qk_layer_scaling:
3906
            scale /= self.layer_number
3907
3908

        # Raw attention scores. [b * np, sq, sk]
3909
3910
3911
3912
3913
3914
        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,
3915
                alpha=scale,
3916
            ).view(*output_size)
3917
3918
3919
3920
3921
3922
3923

        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]
            )
3924
            matmul_result = matmul_result.view(*output_size) + core_attention_bias
3925
            matmul_result *= scale
3926

3927
3928
3929
3930
        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":
3931
3932
                _, core_attention_bias = dpa_utils.get_alibi(
                    _alibi_cache,
3933
3934
3935
                    output_size[1],
                    output_size[2],
                    output_size[3],
3936
3937
                    actual_seqlens_q=actual_seqlens_q if "padding" in attn_mask_type else None,
                    actual_seqlens_kv=actual_seqlens_kv if "padding" in attn_mask_type else None,
3938
3939
                    alibi_slopes=alibi_slopes,
                    bottom_right_alignment=attn_mask_type not in ["causal", "padding_causal"],
3940
                )
3941
3942
3943
3944
3945
            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,
3946
                alpha=scale,
3947
            )
3948
3949
            matmul_result = (matmul_result.view(*output_size) + core_attention_bias).to(
                dtype=query_layer.dtype
3950
            )
3951
3952
3953

        # attention scores and attention mask [b, np, sq, sk]
        softmax_scale = self.layer_number if apply_qk_layer_scaling else None
3954
        attention_probs = self.scale_mask_softmax(
3955
            matmul_result, attention_mask, attn_mask_type, softmax_scale
3956
        )
3957

3958
3959
3960
3961
3962
        # mask out the pad positions in softmax results, mostly for the rows (pad tokens from q)
        # the columns (pad tokens from k) are already zeroed out during softmax
        if "padding" in attn_mask_type:
            attention_probs = attention_probs.masked_fill(attention_mask, 0)

3963
3964
3965
3966
3967
3968
3969
3970
3971
3972
3973
3974
3975
3976
3977
        # 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]
3978
        value_layer = value_layer.reshape(value_layer.size(0), output_size[0] * output_size[1], -1)
3979
3980

        # change view [b * np, sq, sk]
3981
        attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
3982
3983
3984
3985
3986
3987
3988

        # 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)

3989
        if q_format == "sbhd":
3990
3991
            # [b, np, sq, hn] --> [sq, b, np, hn]
            context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
3992

3993
3994
3995
            # [sq, b, np, hn] --> [sq, b, hp]
            context_layer = context_layer.view(seqlen, batch_size, -1)

3996
        if q_format == "bshd":
3997
3998
3999
4000
4001
            # [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)
4002

4003
4004
4005
4006
4007
4008
4009
4010
4011
4012
4013
4014
4015
4016
        if q_format == "thd":
            # [b, np, sq, hn] --> [b, sq, np, hn]
            context_layer = context_layer.permute(0, 2, 1, 3).contiguous()

            # [b, sq, np, hn] --> [tq, np, hn]
            context_layer = tex.convert_bshd_to_thd(
                context_layer,
                cu_seqlens_q,
                total_tokens,
            )

            # [tq, np, hn] --> [tq, hp]
            context_layer = context_layer.view(total_tokens, -1)

4017
4018
4019
4020
4021
        return context_layer


class _PrepareQKVForFA(torch.autograd.Function):
    """This class converts QKV from interleaved (s, b, ...) layout
4022
    to separate contiguous q, k, v tensors in (b, s, ...) layout."""
4023
4024

    @staticmethod
4025
4026
4027
4028
    def forward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
4029
        value_layer: torch.Tensor,
4030
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
4031
        # pylint: disable=missing-function-docstring
4032
4033
4034
4035
4036
4037
4038
4039
4040
4041
4042
        # 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
4043
4044
4045
4046
    def backward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        dq: torch.Tensor,
        dk: torch.Tensor,
4047
        dv: torch.Tensor,
4048
    ) -> Tuple[Union[torch.Tensor, None], ...]:
4049
        # pylint: disable=missing-function-docstring
4050
4051
4052
4053
        dqkv = tex.fa_prepare_bwd(dq, dk, dv)
        dq, dk, dv = split_tensor_along_dim(dqkv, -1, 3)
        return dq, dk, dv

4054

4055
class FlashAttention(torch.nn.Module):
4056
    """Dot product attention, using HazyResearch flash-attn package:
4057
    https://github.com/Dao-AILab/flash-attention
4058
4059
4060
4061
    """

    def __init__(
        self,
4062
        softmax_scale: float,
4063
4064
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
4065
4066
        attention_type: str = "self",
        layer_number: Optional[int] = None,
4067
        deterministic: bool = False,
4068
4069
4070
    ) -> None:
        super().__init__()

4071
        if fa_utils.is_installed:
4072
            assert (
4073
4074
                fa_utils.version >= fa_utils.version_required
            ), f"FlashAttention minimum version {fa_utils.version_required} is required."
4075
            assert (
4076
4077
                fa_utils.version <= fa_utils.max_version
            ), f"FlashAttention maximum version {fa_utils.max_version} is supported."
4078

4079
        self.softmax_scale = softmax_scale
4080
4081
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_dropout = attention_dropout
4082
4083
        self.attention_type = attention_type
        self.layer_number = 1 if layer_number is None else layer_number
4084
        self.deterministic = deterministic
4085
        self.logger = logging.getLogger("FlashAttention")
4086
        self.logger.setLevel(attn_log._log_level)
4087
        if not self.logger.hasHandlers():
4088
            self.logger.addHandler(attn_log._stream_handler)
4089
4090
4091
4092
4093
4094

    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
4095
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
4096
4097
4098
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
4099
4100
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
4101
        attn_mask_type: str = "causal",
4102
        window_size: Optional[Tuple[int, int]] = None,
4103
        alibi_slopes: Optional[torch.Tensor] = None,
4104
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
4105
        cp_global_ranks: List[int] = None,
4106
        cp_stream: torch.cuda.Stream = None,
4107
        cp_comm_type: str = "p2p",
4108
4109
        fp8: bool = False,
        fp8_meta: Optional[Dict[str, Any]] = None,
4110
        quantizers=None,
4111
4112
        inference_params: Optional[InferenceParams] = None,
        flash_attention_backend: Optional[PkgVersion] = PkgVersion("0"),
4113
4114
4115
    ) -> torch.Tensor:
        """flash-attn fprop"""

4116
4117
4118
4119
        assert all(
            x.dtype in [torch.float16, torch.bfloat16] or isinstance(x, Float8Tensor)
            for x in [query_layer, key_layer, value_layer]
        ), "FlashAttention only supports FP16 and BF16 data types, or Float8Tensors."
4120
4121
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
4122
        ), "FlashAttention currently only supports CUDA tensors."
4123
4124
        assert (
            qkv_layout in QKVLayouts
4125
        ), f"FlashAttention does not support qkv_layout = {qkv_layout}!"
4126

4127
4128
4129
4130
4131
4132
        cp_size = 1
        if isinstance(cp_group, dist_group_type):
            cp_size = get_distributed_world_size(cp_group)
        elif isinstance(cp_group, list):
            for group in cp_group:
                cp_size *= get_distributed_world_size(group)
4133
        context_parallel = cp_size > 1
4134

4135
4136
        # get q_format and kv_format for training and inference
        qkv_format, q_format, kv_format = dpa_utils.get_qkv_format(qkv_layout, inference_params)
4137

4138
        # convert q, k, v to bshd if they are in sbhd; qkv_format doesn't change
4139
4140
4141
4142
4143
4144
4145
4146
4147
4148
4149
4150
4151
        if all(not isinstance(x, Float8Tensor) for x in [query_layer, key_layer, value_layer]):
            if qkv_format == "sbhd":
                # For now just 128, will make it more general in the future
                if (
                    query_layer.shape[-1] == 128
                    and query_layer.shape[0] * query_layer.shape[1] >= 512
                    and qkv_layout == "sbh3d"
                ):
                    query_layer, key_layer, value_layer = _PrepareQKVForFA.apply(
                        query_layer, key_layer, value_layer
                    )
                else:
                    query_layer, key_layer, value_layer = [
4152
4153
                        x.transpose(0, 1).contiguous()
                        for x in (query_layer, key_layer, value_layer)
4154
                    ]
4155
4156
            elif q_format == "sbhd" and kv_format == "bshd":
                query_layer = query_layer.transpose(0, 1).contiguous()
4157
            if context_parallel:
4158
                query_layer, key_layer, value_layer = [
4159
4160
4161
4162
4163
                    x.contiguous() for x in (query_layer, key_layer, value_layer)
                ]
        else:
            if qkv_format == "sbhd":
                query_layer._data, key_layer._data, value_layer._data = [
4164
                    x.transpose(0, 1).contiguous()
4165
4166
                    for x in (query_layer._data, key_layer._data, value_layer._data)
                ]
4167
                query_layer, key_layer, value_layer = [
4168
                    Float8Tensor.make_like(x, data=x._data, shape=x._data.shape)
4169
4170
                    for x in (query_layer, key_layer, value_layer)
                ]
4171
4172
4173
4174
4175
            elif q_format == "sbhd" and kv_format == "bshd":
                query_layer._data = query_layer._data.transpose(0, 1).contiguous()
                query_layer = Float8Tensor.make_like(
                    query_layer, data=query_layer._data, shape=query_layer._data.shape
                )
4176
            if context_parallel:
4177
4178
                query_layer._data, key_layer._data, value_layer._data = [
                    x.contiguous() for x in (query_layer._data, key_layer._data, value_layer._data)
4179
                ]
4180

4181
4182
4183
4184
4185
4186
4187
4188
        # get batch_size, max_seqlen and cu_seqlens
        batch_size, context_len = None, None
        if inference_params is None:
            if qkv_format in ["sbhd", "bshd"]:
                batch_size = query_layer.shape[0]
                max_seqlen_q, max_seqlen_kv = query_layer.shape[1], key_layer.shape[1]
                max_seqlen_q *= cp_size
                max_seqlen_kv *= cp_size
4189

4190
4191
4192
4193
                if "padding" in attn_mask_type:
                    assert (
                        not context_parallel
                    ), "Padding mask not supported with context parallelism!"
4194

4195
4196
4197
4198
4199
                    # [b * s, h, d]
                    query_layer, key_layer, value_layer = [
                        x.reshape(x.shape[0] * x.shape[1], *x.shape[2:])
                        for x in [query_layer, key_layer, value_layer]
                    ]
4200

4201
                    if self.attention_type == "self":
4202
                        assert (
4203
4204
4205
4206
4207
4208
4209
4210
4211
4212
4213
4214
4215
4216
                            max_seqlen_q == max_seqlen_kv
                        ), "Maximum sequence length for Q and KV should be the same."
                        if cu_seqlens_q is None:
                            assert (
                                attention_mask is not None
                            ), "Please provide attention_mask for padding!"
                            cu_seqlens_q, indices_q = dpa_utils.get_cu_seqlens_and_indices(
                                attention_mask
                            )
                        else:
                            indices_q = dpa_utils.get_indices(max_seqlen_q, cu_seqlens_q)
                        cu_seqlens_kv = cu_seqlens_q
                        query_layer, key_layer, value_layer = dpa_utils.PackTensors.apply(
                            indices_q, query_layer, key_layer, value_layer
4217
                        )
4218
                    else:
4219
4220
4221
4222
4223
4224
4225
4226
4227
4228
4229
4230
4231
4232
4233
4234
4235
                        if cu_seqlens_q is None or cu_seqlens_kv is None:
                            assert (
                                attention_mask is not None
                            ), "Please provide attention_mask for padding!"
                            cu_seqlens_q, indices_q = dpa_utils.get_cu_seqlens_and_indices(
                                attention_mask[0]
                            )
                            cu_seqlens_kv, indices_kv = dpa_utils.get_cu_seqlens_and_indices(
                                attention_mask[1]
                            )
                        else:
                            indices_q = dpa_utils.get_indices(max_seqlen_q, cu_seqlens_q)
                            indices_kv = dpa_utils.get_indices(max_seqlen_kv, cu_seqlens_kv)
                        query_layer = dpa_utils.PackTensors.apply(indices_q, query_layer)
                        key_layer, value_layer = dpa_utils.PackTensors.apply(
                            indices_kv, key_layer, value_layer
                        )
4236
                else:
4237
4238
4239
4240
4241
4242
                    # Cumulative sequence lengths for unpadded data
                    if cu_seqlens_q is None:
                        cu_seqlens_q = dpa_utils.get_full_cu_seqlens(
                            batch_size,
                            max_seqlen_q,
                            query_layer.device,
4243
                        )
4244
4245
4246
4247
4248
                    if cu_seqlens_kv is None:
                        cu_seqlens_kv = dpa_utils.get_full_cu_seqlens(
                            batch_size,
                            max_seqlen_kv,
                            key_layer.device,
4249
                        )
4250
4251
4252
4253
4254
4255
4256
4257
4258
4259
4260
4261
4262
4263
4264
4265
4266
4267
4268
4269
4270
4271
4272
            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!"
                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()
        else:
            if qkv_format in ["sbhd_2bshd", "bshd"]:
                # q is in bshd in both cases from conversion above or the original input
                batch_size, context_len = query_layer.shape[:2]
                cu_seqlens_q = cu_seqlens_q[: batch_size + 1]
                cu_seqlens_kv = cu_seqlens_kv[: batch_size + 1]
                # convert from bshd to thd_2bshd for flash_attn_varlen_func/_with_kvcache;
                # kernel assumes tensor is contiguous
                if isinstance(query_layer, Float8Tensor):
                    query_layer._data = tex.convert_bshd_to_thd(
                        query_layer._data,
                        cu_seqlens_q,
                        batch_size * context_len,
4273
                    )
4274
4275
                    query_layer = Float8Tensor.make_like(
                        query_layer, data=query_layer._data, shape=query_layer._data.shape
4276
                    )
4277
4278
4279
4280
4281
                else:
                    query_layer = tex.convert_bshd_to_thd(
                        query_layer,
                        cu_seqlens_q,
                        batch_size * context_len,
4282
                    )
4283

4284
4285
4286
        use_flash_attn_3 = False
        if flash_attention_backend is not None and flash_attention_backend > PkgVersion("3.0.0b"):
            use_flash_attn_3 = True
4287
4288
4289
        if context_parallel and all(
            not isinstance(x, Float8Tensor) for x in [query_layer, key_layer, value_layer]
        ):
4290
4291
4292
            assert (
                alibi_slopes is None
            ), "Alibi slope bias addition is not supported with context parallelism."
4293
            with self.attention_dropout_ctx():
4294
                output = attn_forward_func_with_cp(
4295
4296
4297
4298
4299
4300
4301
4302
                    self.training,
                    query_layer,
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
4303
4304
                    cu_seqlens_q if qkv_format == "thd" else None,
                    cu_seqlens_kv if qkv_format == "thd" else None,
4305
                    self.attention_dropout if self.training else 0.0,
4306
4307
4308
                    cp_group,
                    cp_global_ranks,
                    cp_stream,
4309
                    cp_comm_type,
4310
                    softmax_scale=self.softmax_scale,
4311
                    qkv_format="bshd" if qkv_format == "sbhd" else qkv_format,
4312
                    attn_mask_type=attn_mask_type,
4313
                    deterministic=self.deterministic,
4314
                    window_size=window_size,
4315
                    quantizers=quantizers,
4316
                    pad_between_seqs=False,
4317
                    use_flash_attn_3=use_flash_attn_3,
4318
4319
                )
        else:
4320
4321

            from .cpu_offload import CPUOffloadEnabled
4322

4323
4324
4325
4326
4327
4328
            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

4329
            with self.attention_dropout_ctx():
4330
4331
4332
4333
4334
4335
4336
4337
4338
4339
4340
4341
                #       | API                     | use cases
                # ----------------------------------------------------------------------
                # FA v2 | flash_attn_func         | bshd/sbhd + not padding
                #       | flash_attn_varlen_func  | bshd/sbhd + padding
                #       |                         | thd + padding
                #       |                         | KV cache (not-paged/paged), i.e.
                #       |                         |     bshd/sbhd/thd + padding
                # FA v3 | flash_attn_func         | bshd/sbhd + not padding
                #       | flash_attn_varlen_func  | bshd/sbhd + padding
                #       |                         | thd + padding
                #       | flash_attn_with_kvcache | KV cache (not-paged/paged), i.e.
                #       |                         |     bshd/sbhd/thd + padding
4342
4343
4344
                fa_optional_forward_args_thd = []
                if qkv_format in ["bshd", "sbhd"] and "padding" not in attn_mask_type:
                    func = (
4345
4346
4347
4348
4349
4350
4351
4352
4353
4354
4355
4356
4357
4358
4359
4360
4361
4362
4363
4364
4365
4366
4367
4368
4369
4370
4371
4372
4373
4374
4375
4376
4377
4378
4379
4380
4381
4382
4383
4384
                        flash_attn_func if not use_flash_attn_3 else flash_attn_func_v3
                    )  # pylint: disable=possibly-used-before-assignment
                else:
                    if not use_flash_attn_3:
                        func = flash_attn_varlen_func
                    elif inference_params is None:
                        func = flash_attn_varlen_func_v3  # pylint: disable=possibly-used-before-assignment
                    else:
                        func = flash_attn_with_kvcache_v3  # pylint: disable=possibly-used-before-assignment
                    if not use_flash_attn_3 or inference_params is None:
                        fa_optional_forward_args_thd.append(cu_seqlens_q)
                        fa_optional_forward_args_thd.append(cu_seqlens_kv)
                        fa_optional_forward_args_thd.append(max_seqlen_q)
                        fa_optional_forward_args_thd.append(max_seqlen_kv)
                if not use_flash_attn_3:
                    fa_optional_forward_kwargs = {}
                    if fa_utils.v2_3_plus:
                        fa_optional_forward_kwargs["window_size"] = window_size
                    if fa_utils.v2_4_plus:
                        fa_optional_forward_kwargs["alibi_slopes"] = alibi_slopes
                    if fa_utils.v2_4_1_plus:
                        fa_optional_forward_kwargs["deterministic"] = self.deterministic
                    if inference_params is not None:
                        # use block_table kwarg to support thd_2bshd for non-paged
                        fa_optional_forward_kwargs["block_table"] = (
                            inference_params.cache_manager.page_table[:batch_size]
                            if inference_params.is_paged
                            else inference_params.cache_manager.batch_indices_post_step.unsqueeze(
                                1
                            )[:batch_size]
                        )
                    output = func(
                        query_layer,
                        key_layer,
                        value_layer,
                        *fa_optional_forward_args_thd,
                        self.attention_dropout if self.training else 0.0,
                        softmax_scale=self.softmax_scale,
                        causal="causal" in attn_mask_type,
                        **fa_optional_forward_kwargs,
4385
                    )
4386
                else:
4387
4388
                    fa_3_optional_forward_kwargs = {}
                    fa_3_optional_forward_kwargs["window_size"] = window_size
4389
4390
4391
4392
4393
4394
4395
4396
4397
4398
4399
4400
                    if inference_params is None:
                        fa_3_optional_forward_kwargs["deterministic"] = self.deterministic
                    else:
                        fa_3_optional_forward_kwargs["cu_seqlens_q"] = cu_seqlens_q
                        fa_3_optional_forward_kwargs["max_seqlen_q"] = max_seqlen_q
                        cache_seqlens = cu_seqlens_kv[1:] - cu_seqlens_kv[:-1]
                        fa_3_optional_forward_kwargs["cache_seqlens"] = cache_seqlens
                        # flash_attn_with_kvcache accepts thd_2bshd for non-paged
                        if inference_params.is_paged:
                            fa_3_optional_forward_kwargs["page_table"] = (
                                inference_params.cache_manager.page_table[:batch_size]
                            )
4401
                    if fp8:
4402
                        QKV_quantizer = quantizers["scaling_fwd"][META_QKV]
4403
                        torch_dtype = get_fp8_torch_dtype(fp8_meta["recipe"], fprop_tensor=True)
4404
                        torch_orig_dtype = query_layer.dtype
4405
4406
4407
4408
4409
4410
4411
4412
4413
4414
4415

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

4416
4417
4418
4419
4420
                        # "fp8_mha" decides outputs in fp8, while inputs are inferred from
                        # the real dtype
                        assert isinstance(key_layer, query_layer.__class__) and isinstance(
                            value_layer, query_layer.__class__
                        ), "q, k, and v must have the same type."
4421
                        if not isinstance(query_layer, Float8Tensor):
4422
                            query_layer, key_layer, value_layer = (
4423
                                QKV_quantizer(x) for x in [query_layer, key_layer, value_layer]
4424
                            )
4425
4426
4427
4428
                        batch_size = cu_seqlens_q.shape[0] - 1
                        num_heads_k = key_layer.shape[-2]
                        fa_3_optional_forward_kwargs["q_descale"] = (
                            query_layer._scale_inv.unsqueeze(0).repeat(batch_size, num_heads_k)
4429
                        )
4430
                        fa_3_optional_forward_kwargs["k_descale"] = key_layer._scale_inv.unsqueeze(
4431
                            0
4432
4433
4434
                        ).repeat(batch_size, num_heads_k)
                        fa_3_optional_forward_kwargs["v_descale"] = (
                            value_layer._scale_inv.unsqueeze(0).repeat(batch_size, num_heads_k)
4435
                        )
4436
4437
4438
                        query_layer, key_layer, value_layer = (
                            convert_to_torch_float8(x, torch_dtype)
                            for x in [query_layer, key_layer, value_layer]
4439
                        )
4440
                    try:
4441
                        output = func(
4442
4443
4444
4445
4446
4447
4448
4449
                            query_layer,
                            key_layer,
                            value_layer,
                            *fa_optional_forward_args_thd,
                            softmax_scale=self.softmax_scale,
                            causal="causal" in attn_mask_type,
                            **fa_3_optional_forward_kwargs,
                        )
4450
4451
                        if isinstance(output, (List, Tuple)):
                            output = output[0]
4452
                    except TypeError as e:
4453
                        if fa_utils.v3_0_0_beta:
4454
4455
4456
4457
                            e.args = (
                                e.args[0]
                                + ". Please update your flash-attn v3 (beta) installation as it "
                                + "may have added more supported arguments to its API. \n"
4458
                                + fa_utils.v3_installation_steps,
4459
4460
4461
4462
4463
4464
4465
4466
                            ) + e.args[1:]
                        raise

                    if fp8:
                        output = output.to(dtype=torch_orig_dtype)
                    if fp8 and fp8_meta["recipe"].fp8_mha:
                        O_quantizer = quantizers["scaling_fwd"][META_O]
                        output = O_quantizer(output)
4467

4468
4469
4470
4471
4472
4473
4474
4475
4476
4477
4478
4479
4480
4481
4482
4483
4484
4485
4486
4487
4488
        if inference_params is None:
            if qkv_format in ["sbhd", "bshd"] and "padding" in attn_mask_type:
                output = dpa_utils.UnpackTensor.apply(indices_q, batch_size * max_seqlen_q, output)
        elif qkv_format in ["bshd", "sbhd_2bshd"]:
            # all KV caching cases use thd_2bshd for calculation
            # convert results back to bshd from thd_2bshd
            if isinstance(query_layer, Float8Tensor):
                output._data = tex.convert_thd_to_bshd(
                    output._data,
                    cu_seqlens_q,
                    batch_size,
                    context_len,
                )
                output = Float8Tensor.make_like(output, data=output._data, shape=output._data.shape)
            else:
                output = tex.convert_thd_to_bshd(
                    output,
                    cu_seqlens_q,
                    batch_size,
                    context_len,
                )
4489

4490
        if q_format == "sbhd":
4491
4492
4493
4494
4495
4496
4497
4498
4499
4500
4501
4502
4503
4504
            # (bs)hd -> bs(hd) -> sb(hd)
            if fp8 and fp8_meta["recipe"].fp8_mha:
                output_data = (
                    output._data.reshape(batch_size, max_seqlen_q // cp_size, -1)
                    .transpose(0, 1)
                    .contiguous()
                )
                output = Float8Tensor.make_like(
                    output,
                    data=output_data,
                    shape=output_data.shape,
                )
            else:
                output = output.view(batch_size, max_seqlen_q // cp_size, -1).transpose(0, 1)
4505
        elif q_format == "bshd":
4506
4507
            # (bs)hd -> bs(hd)
            output = output.reshape(batch_size, max_seqlen_q // cp_size, -1)
4508
        elif q_format == "thd":
4509
4510
4511
4512
4513
4514
4515
4516
4517
4518
4519
4520
4521
4522
4523
4524
4525
4526
4527
4528
4529
4530
4531
4532
4533
4534
4535
4536
4537
4538
4539
4540
            # thd -> t(hd)
            output = output.reshape(output.shape[0], -1)

        return output.contiguous()


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

    num_tensors = len(tensors)
    new_shape = list(tensors[0].shape)
    new_shape.insert(dim, num_tensors)
    if isinstance(tensors[0], Float8Tensor):
        new_stride = list(tensors[0]._data.stride())
        new_stride.insert(dim, int(new_stride[dim - 1] / num_tensors))
        combined_tensor = torch.Tensor().to(device=tensors[0].device, dtype=tensors[0]._data.dtype)
        combined_tensor.set_(
            tensors[0]._data.untyped_storage(),
            tensors[0]._data.storage_offset(),
            new_shape,
            new_stride,
        )
        combined_tensor = Float8Tensor.make_like(tensors[0], data=combined_tensor, shape=new_shape)
    else:
        new_stride = list(tensors[0].stride())
        new_stride.insert(dim, int(new_stride[dim - 1] / num_tensors))
        combined_tensor = torch.Tensor().to(device=tensors[0].device, dtype=tensors[0].dtype)
        combined_tensor.set_(
            tensors[0].untyped_storage(), tensors[0].storage_offset(), new_shape, new_stride
4541
4542
        )

4543
4544
    return combined_tensor

4545

4546
4547
4548
4549
class FusedAttnFunc(torch.autograd.Function):
    """Function for FusedAttention with separate Q, K, V tensors"""

    @staticmethod
4550
4551
4552
4553
4554
4555
4556
    def forward(
        ctx,
        is_training,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q,
        cu_seqlens_kv,
4557
4558
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
4559
4560
        page_table_k,
        page_table_v,
4561
4562
4563
4564
4565
4566
4567
4568
4569
4570
        q,
        k,
        v,
        attn_bias,
        attn_scale,
        dropout_p,
        fast_zero_fill,
        qkv_layout,
        attn_bias_type,
        attn_mask_type,
4571
        window_size,
4572
4573
4574
4575
4576
        rng_gen,
        fused_attention_backend,
        use_FAv2_bwd,
        fp8,
        fp8_meta,
4577
        quantizers,
4578
        deterministic,
4579
    ):
4580
        # pylint: disable=missing-function-docstring
4581
        # "fp8_mha" decides outputs in fp8, while inputs are inferred from the real dtype
4582
        is_input_fp8 = False
4583
        is_output_fp8 = fp8_meta["recipe"].fp8_mha if "recipe" in fp8_meta else False
4584
4585
4586
4587

        # FP16/BF16 attn:                  fake_dtype = torch.float16 or torch.bfloat16
        # FP8 attn, is_output_fp8 = False: fake_dtype = torch.float16 or torch.bfloat16
        # FP8 attn, is_output_fp8 = True:  fake_dtype = torch.float8_e4m3fn
4588
4589
4590
        fake_dtype = q.dtype

        QKV_quantizer, O_quantizer, S_quantizer, dQKV_quantizer, dO_quantizer, dP_quantizer = (
4591
            dpa_utils.get_attention_quantizers(fp8, quantizers, cp_specific_quantizers=False)
4592
        )
4593
4594
        if fp8:
            fused_attention_backend = FusedAttnBackend["FP8"]
4595
4596
4597
            assert isinstance(k, q.__class__) and isinstance(
                v, q.__class__
            ), "q, k, and v must have the same type."
4598

4599
            is_input_fp8 = isinstance(q, Float8Tensor)
4600
            q_fp8, k_fp8, v_fp8 = None, None, None
4601
            if is_input_fp8:
4602
                q_fp8, k_fp8, v_fp8 = q, k, v
4603
4604
            else:
                # 1: qkv packed, 2: kv packed, 3: qkv separate
4605
                qkv_group = len(qkv_layout.replace("paged_kv_", "").split("_"))
4606
4607
4608
4609
4610
4611
4612
4613
4614
4615
4616
4617
4618
4619
4620
4621
4622
4623
4624
4625
                match qkv_group:
                    case 1:
                        dim = qkv_layout.find("3")
                        qkv = _combine_tensors([q, k, v], dim)
                        qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
                        qkv_fp8 = QKV_quantizer(qkv)
                        q_fp8, k_fp8, v_fp8 = _SplitAlongDim.apply(qkv_fp8, dim, [1, 1, 1], True)
                    case 2:
                        q_fp8 = QKV_quantizer(q)
                        dim = qkv_layout.split("_")[1].find("2")
                        kv = _combine_tensors([k, v], dim)
                        kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
                        kv_fp8 = QKV_quantizer(kv_c)
                        k_fp8, v_fp8 = _SplitAlongDim.apply(kv_fp8, dim, [1, 1], True)
                    case 3:
                        q_fp8 = QKV_quantizer(q)
                        k_fp8 = QKV_quantizer(k)
                        v_fp8 = QKV_quantizer(v)
                    case _:
                        raise "Invalid qkv_layout " + qkv_layout
4626
            # q_fp8, k_fp8, v_fp8, out_fp8: torch.float8_e4m3fn
4627
            out_fp8, aux_ctx_tensors = fused_attn_fwd(
4628
4629
4630
4631
4632
4633
4634
4635
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q_fp8,
                k_fp8,
                v_fp8,
4636
                fake_dtype,
4637
4638
                fused_attention_backend,
                attn_bias,
4639
4640
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
4641
4642
                None,
                None,
4643
4644
                S_quantizer,
                O_quantizer,
4645
4646
4647
4648
4649
4650
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
4651
                window_size,
4652
4653
                rng_gen,
            )
4654
            if is_output_fp8:
4655
                out_ret = out_fp8
4656
            else:
4657
                out_ret = out_fp8.dequantize().view(out_fp8.shape)
4658
4659
            # is_output_fp8 = False: out_save.dtype = torch.float16 or torch.bfloat16
            # is_output_fp8 = True:  out_save.dtype = torch.float8_e4m3fn
4660
4661
            out_save = out_ret

4662
            if not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
4663
                # 1: qkv packed, 2: kv packed, 3: qkv separate
4664
                if is_input_fp8:
4665
                    qkv_group = len(qkv_layout.replace("paged_kv_", "").split("_"))
4666
4667
4668
4669
                    if qkv_group == 1:
                        dim = qkv_layout.find("3")
                        qkv = _combine_tensors([q, k, v], dim)
                        qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
4670
4671
                        qkv_no_fp8 = qkv_c.dequantize().view(qkv.shape)
                        q, k, v = _SplitAlongDim.apply(qkv_no_fp8, dim, [1, 1, 1], True)
4672
                    if qkv_group == 2:
4673
                        q = q.dequantize()
4674
                        dim = qkv_layout.replace("paged_kv_", "").split("_")[1].find("2")
4675
4676
                        kv = _combine_tensors([k, v], dim)
                        kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
4677
4678
                        kv_no_fp8 = kv.dequantize()
                        k, v = _SplitAlongDim.apply(kv_no_fp8, dim, [1, 1], True)
4679
                    if qkv_group == 3:
4680
4681
4682
                        q = q.dequantize()
                        k = k.dequantize()
                        v = v.dequantize()
4683
                if is_output_fp8:
4684
4685
4686
                    out_save = out_fp8.dequantize()

            fp8_tensors = (q_fp8, k_fp8, v_fp8, out_fp8)
4687
        else:
4688
            # q, k, v, out_ret: torch.float16 or torch.bfloat16
4689
            out_ret, aux_ctx_tensors = fused_attn_fwd(
4690
4691
4692
4693
4694
4695
4696
4697
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q,
                k,
                v,
4698
                fake_dtype,
4699
4700
                fused_attention_backend,
                attn_bias,
4701
4702
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
4703
4704
                page_table_k,
                page_table_v,
4705
4706
                None,  # s_quantizer
                None,  # o_quantizer
4707
4708
4709
4710
4711
4712
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
4713
                window_size,
4714
4715
                rng_gen,
            )
4716
            out_save = out_ret
4717
            fp8_tensors = (None, None, None, None)
4718

4719
4720
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))

4721
        from .cpu_offload import CPUOffloadEnabled
4722

4723
        if CPUOffloadEnabled:
4724
4725
4726
4727
4728
4729
4730
            if ctx.fp8:
                tensor_list = fp8_tensors
            else:
                tensor_list = [q, k, v, out_save]

            tensor_list.extend(aux_ctx_tensors)

4731
            qkv_layout = "sbhd_sbhd_sbhd"
4732
4733
4734
4735
            for tensor in tensor_list:
                if tensor is not None:
                    tensor.activation_offloading = True

4736
4737
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
4738
        qkvo_tensors = (q, k, v, out_save) if not ctx.fp8 else (None, None, None, None)
4739
4740
        tensors_to_save, tensor_objects = prepare_for_saving(
            *fp8_tensors,
4741
4742
4743
            *qkvo_tensors,
            cu_seqlens_q,
            cu_seqlens_kv,
4744
4745
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
4746
4747
            *aux_ctx_tensors,
        )
4748
4749
        ctx.save_for_backward(*tensors_to_save)
        ctx.tensor_objects = tensor_objects
4750
        ctx.fp8_meta = fp8_meta
4751
4752
4753
4754
4755

        ctx.dQKV_quantizer = dQKV_quantizer
        ctx.dO_quantizer = dO_quantizer
        ctx.dP_quantizer = dP_quantizer
        ctx.S_quantizer = S_quantizer
4756
4757
4758
        if ctx.fp8:
            ctx.S_quantizer = S_quantizer.copy()
            ctx.S_quantizer.scale = S_quantizer.scale.clone()
4759

4760
4761
4762
4763
4764
4765
4766
4767
        ctx.max_seqlen_q = max_seqlen_q
        ctx.max_seqlen_kv = max_seqlen_kv
        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
4768
        ctx.window_size = window_size
4769
        ctx.fused_attention_backend = (
4770
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
4771
        )
4772
        ctx.use_FAv2_bwd = use_FAv2_bwd
4773
        ctx.deterministic = deterministic
4774

4775
        return out_ret
4776
4777
4778

    @staticmethod
    def backward(ctx, d_out):
4779
        # pylint: disable=missing-function-docstring
4780
        if ctx.is_output_fp8:
4781
4782
4783
            assert isinstance(
                d_out, Float8Tensor
            ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
4784

4785
4786
4787
4788
4789
        # FP16/BF16 attn:                  fake_dtype = torch.float16 or torch.bfloat16
        # FP8 attn, is_output_fp8 = False: fake_dtype = torch.float16 or torch.bfloat16
        # FP8 attn, is_output_fp8 = True:  fake_dtype = torch.float8_e5m2
        fake_dtype = d_out.dtype

4790
        d_out = d_out.contiguous()
4791
        (
4792
4793
4794
4795
            q_fp8,
            k_fp8,
            v_fp8,
            out_fp8,
4796
4797
4798
4799
4800
4801
            q,
            k,
            v,
            out,
            cu_seqlens_q,
            cu_seqlens_kv,
4802
4803
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
4804
4805
4806
4807
4808
            *other_tensors,
        ) = restore_from_saved(ctx.tensor_objects, ctx.saved_tensors)

        aux_ctx_tensors = other_tensors

4809
4810
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
4811
        rest = [None]
4812
        if ctx.use_FAv2_bwd:
4813
            softmax_lse, rng_state = aux_ctx_tensors
4814
4815
4816
            dq = torch.empty_like(q)
            dk = torch.empty_like(k)
            dv = torch.empty_like(v)
4817
            d_out, q, k, v, out = [maybe_contiguous(x) for x in (d_out, q, k, v, out)]
4818
            flash_attn_cuda_bwd(
4819
4820
4821
4822
4823
4824
4825
4826
4827
4828
4829
4830
4831
4832
4833
4834
4835
4836
4837
                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,
4838
            )
4839
4840
4841
            dq = dq[..., : d_out.shape[-1]]
            dk = dk[..., : d_out.shape[-1]]
            dv = dv[..., : d_out.shape[-1]]
4842
        else:
4843
4844
            with torch.cuda.nvtx.range("_FusedAttn"):
                if ctx.fp8:
4845
                    if ctx.is_output_fp8:
4846
4847
                        d_out_fp8 = d_out
                    else:
4848
                        d_out_fp8 = ctx.dO_quantizer(d_out)
4849
4850
4851
                    dqkv_dtype = TE_DType[d_out_fp8._data.dtype]
                    # q_fp8, k_fp8, v_fp8, out_fp8:      torch.float8_e4m3fn
                    # d_out_fp8, dq_fp8, dk_fp8, dv_fp8: torch.float8_e5m2
4852
                    dq_fp8, dk_fp8, dv_fp8, *rest = fused_attn_bwd(
4853
4854
4855
4856
4857
4858
4859
4860
4861
                        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,
4862
4863
                        fake_dtype,
                        dqkv_dtype,
4864
                        aux_ctx_tensors,
4865
                        ctx.fused_attention_backend,
4866
4867
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
4868
4869
4870
                        ctx.S_quantizer,
                        ctx.dP_quantizer,
                        ctx.dQKV_quantizer,
4871
4872
4873
4874
4875
4876
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
4877
4878
                        ctx.window_size,
                        ctx.deterministic,
4879
                    )
4880

4881
4882
                    # is_input_fp8 = False: dq, dk, dv: torch.float16 or torch.bfloat16
                    # is_input_fp8 = True:  dq, dk, dv: torch.float8_e5m2
4883
                    if not ctx.is_input_fp8:
4884
                        qkv_group = len(ctx.qkv_layout.replace("paged_kv_", "").split("_"))
4885
                        if qkv_group == 1:
4886
                            dim = ctx.qkv_layout.find("3")
4887
4888
                            dqkv_fp8_data = _combine_tensors(
                                [dq_fp8._data, dk_fp8._data, dv_fp8._data], dim
4889
                            )
4890
4891
4892
4893
4894
                            dqkv_fp8 = dq_fp8.make_like(
                                tensor=dq_fp8, data=dqkv_fp8_data, shape=dqkv_fp8_data.shape
                            )
                            dqkv = dqkv_fp8.dequantize()
                            dq, dk, dv = _SplitAlongDim.apply(dqkv, dim, [1, 1, 1], True)
4895
                        if qkv_group == 2:
4896
                            dq = dq_fp8.dequantize()
4897
4898
4899
4900
4901
                            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]
                            )
4902
4903
                            dkv = dkv_c_fp8.dequantize()
                            dk, dv = _SplitAlongDim.apply(dkv, dim, [1, 1], True)
4904
                        if qkv_group == 3:
4905
4906
4907
4908
4909
                            dq = dq_fp8.dequantize()
                            dk = dk_fp8.dequantize()
                            dv = dv_fp8.dequantize()
                    else:
                        dq, dk, dv = dq_fp8, dk_fp8, dv_fp8
4910
                else:
4911
4912
                    if isinstance(d_out, QuantizedTensor):
                        d_out = d_out.dequantize()
4913
4914
                    dqkv_dtype = TE_DType[d_out.dtype]
                    # q, k, v, out, d_out, dq, dk, dv: torch.float16 or torch.bfloat16
4915
                    dq, dk, dv, *rest = fused_attn_bwd(
4916
4917
4918
4919
4920
4921
4922
4923
4924
                        ctx.max_seqlen_q,
                        ctx.max_seqlen_kv,
                        cu_seqlens_q,
                        cu_seqlens_kv,
                        q,
                        k,
                        v,
                        out,
                        d_out,
4925
4926
                        fake_dtype,
                        dqkv_dtype,
4927
                        aux_ctx_tensors,
4928
                        ctx.fused_attention_backend,
4929
4930
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
4931
4932
4933
4934
4935
4936
4937
4938
4939
                        None,
                        None,
                        None,
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
4940
4941
                        ctx.window_size,
                        ctx.deterministic,
4942
                    )
4943

4944
4945
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
4946
4947
4948
4949
4950
4951
4952
4953
            return (
                None,
                None,
                None,
                None,
                None,
                None,
                None,
4954
4955
                None,
                None,
4956
4957
4958
4959
4960
4961
4962
4963
4964
4965
4966
4967
4968
4969
4970
4971
4972
4973
4974
                dq,
                dk,
                dv,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
            )
4975
        # else, return (dqkv, dbias)
4976
4977
4978
4979
4980
4981
4982
4983
        return (
            None,
            None,
            None,
            None,
            None,
            None,
            None,
4984
4985
            None,
            None,
4986
4987
4988
4989
4990
4991
4992
4993
4994
4995
4996
4997
4998
4999
5000
5001
5002
            dq,
            dk,
            dv,
            rest[0],
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
5003
            None,
5004
        )
5005

5006

5007
class FusedAttention(torch.nn.Module):
5008
5009
5010
5011
5012
5013
5014
5015
5016
    """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:

5017
5018
5019
5020
    | backend       | 1                       | 2                              |
    | flash based   | no                      | yes                            |
    | cuDNN based   | yes                     | yes                            |
    | qkv dtype     | fp16/bf16               | fp16/bf16                      |
5021
    | attn_type     | self/cross              | self/cross                     |
5022
    | qkv_layout    |                         |                                |
5023
    |  - (q,k,v)    | sb3hd, bs3hd            | sb3hd, bs3hd, sbh3d, bsh3d     |
5024
    |               | sbhd_sb2hd, bshd_bs2hd  | sbhd_sb2hd, bshd_bs2hd         |
5025
5026
    |               | bshd_bshd_bshd          | sbhd_sbh2d, bshd_bsh2d         |
    |               |                         | sbhd_sbhd_sbhd, bshd_bshd_bshd |
5027
5028
    | mask_type     | causal/padding/no_mask  | causal/padding/no_mask         |
    | bias_type     | post_scale_bias/no_bias | post_scale_bias/alibi/no_bias  |
5029
    | dropout       | yes                     | yes                            |
5030
5031
    | max_seqlen    | <=512, multiple of 64   | any, multiple of 64            |
    | head_dim      | 64                      | <=128, multiple of 8           |
5032
    | output dtype  | fp16/bf16               | fp16/bf16                      |
5033
5034
5035
5036
    """

    def __init__(
        self,
5037
        softmax_scale: float,
5038
5039
5040
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
        attention_type: str = "self",
5041
5042
        layer_number: Optional[int] = None,
        deterministic: bool = False,
5043
5044
5045
    ) -> None:
        super().__init__()

5046
        self.softmax_scale = softmax_scale
5047
5048
5049
        self.attention_dropout = attention_dropout
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_type = attention_type
5050
5051
5052
        self.use_FAv2_bwd = os.getenv(
            "NVTE_FUSED_ATTN_USE_FAv2_BWD", "0"
        ) == "1" and get_device_compute_capability() == (9, 0)
5053
        self.layer_number = 1 if layer_number is None else layer_number
5054
        self.deterministic = deterministic
5055

5056
        def remove_extra_states_check(self, incompatible_keys):  # pylint: disable=unused-argument
5057
5058
            """
            Temporarily remove fused_attention._extra_state as a missing key
5059
            or an unexpected key when loading Transformer Engine checkpoints.
5060
5061
            Please store FP8 metadata as DotProductAttention's _extra_state,
            rather than FusedAttention's _extra_state. This hook will be
5062
            phased out in Transformer Engine 2.0.
5063
5064
            """
            for key in incompatible_keys.missing_keys:
5065
                if "fused_attention._extra_state" in key:
5066
                    incompatible_keys.missing_keys.remove(key)
5067
5068
5069
5070
5071
5072
5073
            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."
                    )
5074

5075
5076
        self.register_load_state_dict_post_hook(remove_extra_states_check)

5077
    @no_torch_dynamo()
5078
5079
5080
5081
5082
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
5083
5084
5085
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
5086
5087
        cu_seqlens_q_padded: Optional[torch.Tensor] = None,
        cu_seqlens_kv_padded: Optional[torch.Tensor] = None,
5088
5089
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
5090
        attn_mask_type: str = "causal",
5091
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
5092
        window_size: Optional[Tuple[int, int]] = None,
5093
        fused_attention_backend: tex.NVTE_Fused_Attn_Backend = tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend,
5094
5095
5096
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
        fast_zero_fill: bool = True,
5097
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
5098
5099
        cp_global_ranks: List[int] = None,
        cp_stream: torch.cuda.Stream = None,
5100
        cp_comm_type: str = "p2p",
5101
5102
        fp8: bool = False,
        fp8_meta: Optional[Dict[str, Any]] = None,
5103
        quantizers=None,
5104
        pad_between_seqs: bool = False,
5105
        inference_params: Optional[InferenceParams] = None,
5106
5107
    ) -> torch.Tensor:
        """fused attention fprop"""
5108
5109
5110
        assert (
            fused_attention_backend != tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend
        ), "No fused attention backend supports this input combination!"
5111
5112
5113
5114
        assert all(
            x.dtype in [torch.float16, torch.bfloat16] or isinstance(x, Float8Tensor)
            for x in [query_layer, key_layer, value_layer]
        ), "FusedAttention only supports FP16 and BF16 data types, or Float8Tensors."
5115
5116
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
5117
        ), "FusedAttention only supports CUDA tensors."
5118
5119
        assert (
            qkv_layout in QKVLayouts
5120
        ), f"FusedAttention does not support qkv_layout = {qkv_layout}!"
5121

5122
5123
5124
5125
5126
5127
        cp_size = 1
        if isinstance(cp_group, dist_group_type):
            cp_size = get_distributed_world_size(cp_group)
        elif isinstance(cp_group, list):
            for group in cp_group:
                cp_size *= get_distributed_world_size(group)
5128
        context_parallel = cp_size > 1
5129

5130
5131
        # get q_format and kv_format for training and inference
        qkv_format, q_format, kv_format = dpa_utils.get_qkv_format(qkv_layout, inference_params)
5132

5133
5134
5135
5136
5137
5138
5139
5140
5141
5142
        # cuDNN can work with 0-length sequences in the batch for both bshd/sbhd and thd formats
        # however, for bshd/sbhd, q/k/v tensors need to have the same batch size as indicated by
        # cu_seqlens, whereas thd does not have this requirement
        # e.g. if q_format = bshd, and q.shape = [3, 1, 16, 64], we should have k.shape[0] =
        # v.shape[0] = q.shape[0], and cu_seqlens_q.shape = cu_seqlens_kv.shape = [4]
        if q_format in ["bshd", "sbhd"] or kv_format in ["bshd", "sbhd"]:
            batch_size = query_layer.shape[0] if q_format == "bshd" else query_layer.shape[1]
            cu_seqlens_q = cu_seqlens_q[: batch_size + 1]
            cu_seqlens_kv = cu_seqlens_kv[: batch_size + 1]

5143
5144
5145
5146
5147
5148
5149
5150
5151
5152
5153
5154
5155
5156
5157
5158
5159
5160
5161
5162
5163
5164
5165
5166
5167
5168
5169
5170
5171
5172
5173
5174
5175
5176
        page_table = None
        if inference_params is None:
            if qkv_format in ["sbhd", "bshd"]:
                if qkv_format == "sbhd":
                    batch_size = query_layer.shape[1]
                    max_seqlen_q = query_layer.shape[0]
                    max_seqlen_kv = key_layer.shape[0]
                if qkv_format == "bshd":
                    batch_size = query_layer.shape[0]
                    max_seqlen_q = query_layer.shape[1]
                    max_seqlen_kv = key_layer.shape[1]
                max_seqlen_q *= cp_size
                max_seqlen_kv *= cp_size
                if "padding" in attn_mask_type:
                    assert (
                        not context_parallel
                    ), "Padding mask not supported with context parallelism!"
                    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!"
                            )
                        if self.attention_type == "self":
                            cu_seqlens_q = dpa_utils.get_cu_seqlens(attention_mask)
                            cu_seqlens_kv = cu_seqlens_q
                        else:
                            cu_seqlens_q = dpa_utils.get_cu_seqlens(attention_mask[0])
                            cu_seqlens_kv = dpa_utils.get_cu_seqlens(attention_mask[1])
                else:
                    if cu_seqlens_q is None:
                        cu_seqlens_q = dpa_utils.get_full_cu_seqlens(
                            batch_size,
                            max_seqlen_q,
                            query_layer.device,
5177
                        )
5178
5179
5180
5181
5182
5183
5184
5185
5186
5187
5188
5189
5190
5191
5192
5193
5194
                    if cu_seqlens_kv is None:
                        cu_seqlens_kv = dpa_utils.get_full_cu_seqlens(
                            batch_size,
                            max_seqlen_kv,
                            key_layer.device,
                        )
            if qkv_format == "thd":
                assert (
                    max_seqlen_q is not None
                    and max_seqlen_kv is not None
                    and cu_seqlens_q is not None
                    and cu_seqlens_kv is not None
                ), "max_seqlen_q/kv and cu_seqlens_q/kv can not be None when qkv_format is thd!"
        elif inference_params.is_paged:
            page_table = inference_params.cache_manager.page_table

        if (q_format == "thd" or "padding" in attn_mask_type) and cu_seqlens_q_padded is None:
5195
            cu_seqlens_q_padded = cu_seqlens_q
5196
        if (kv_format == "thd" or "padding" in attn_mask_type) and cu_seqlens_kv_padded is None:
5197
            cu_seqlens_kv_padded = cu_seqlens_kv
5198

5199
5200
5201
5202
5203
        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)
        )
5204

5205
5206
5207
5208
5209
5210
5211
5212
5213
5214
5215
        if fp8:
            assert fused_attention_backend == tex.NVTE_Fused_Attn_Backend.NVTE_FP8, (
                f"cuDNN attention sub-backend {int(tex.NVTE_Fused_Attn_Backend.NVTE_FP8)}"
                " is required for FP8 attention!"
            )
            assert fp8_meta is not None, "FP8 metadata fp8_meta is required for FP8 attention!"
            assert not context_parallel or fp8_meta["recipe"].reduce_amax, (
                "Amax reduction across TP+CP group is necessary when using context parallelism with"
                " FP8!"
            )

5216
        if context_parallel:
5217
            assert (
5218
5219
                fp8
                or fused_attention_backend == tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen
5220
5221
5222
5223
5224
5225
5226
            ), 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)
            ]
5227
5228
5229
            with self.attention_dropout_ctx():
                output = attn_forward_func_with_cp(
                    self.training,
5230
5231
5232
5233
5234
5235
5236
                    query_layer,
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
5237
5238
                    cu_seqlens_q_padded,
                    cu_seqlens_kv_padded,
5239
                    self.attention_dropout if self.training else 0.0,
5240
5241
5242
                    cp_group,
                    cp_global_ranks,
                    cp_stream,
5243
                    cp_comm_type,
5244
                    softmax_scale=self.softmax_scale,
5245
                    qkv_format=qkv_format,
5246
                    attn_mask_type=attn_mask_type,
5247
5248
                    attn_bias_type=core_attention_bias_type,
                    attn_bias=core_attention_bias,
5249
                    deterministic=self.deterministic,
5250
                    use_fused_attention=True,
5251
                    window_size=window_size,
5252
5253
                    fp8=fp8,
                    fp8_meta=fp8_meta,
5254
                    quantizers=quantizers,
5255
                    pad_between_seqs=pad_between_seqs,
5256
5257
                )
        else:
5258
5259
5260
5261
5262
5263
5264
            with self.attention_dropout_ctx():
                output = FusedAttnFunc.apply(
                    self.training,
                    max_seqlen_q,
                    max_seqlen_kv,
                    cu_seqlens_q,
                    cu_seqlens_kv,
5265
5266
                    cu_seqlens_q_padded,
                    cu_seqlens_kv_padded,
5267
5268
                    page_table,
                    page_table,
5269
5270
5271
5272
5273
5274
5275
5276
5277
5278
                    query_layer,
                    key_layer,
                    value_layer,
                    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,
5279
                    window_size,
5280
5281
5282
5283
5284
                    None,  # rng_gen
                    fused_attention_backend,
                    use_FAv2_bwd,
                    fp8,
                    fp8_meta,
5285
                    quantizers,
5286
                    self.deterministic,
5287
                )
5288

5289
5290
        # ...hd -> ...(hd)
        return output.view(*output.shape[:-2], -1)
5291
5292


5293
class DotProductAttention(TransformerEngineBaseModule):
5294
5295
5296
5297
5298
5299
    """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::

5300
        Argument :attr:`attention_mask` in the `forward` call is only used when
5301
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
5302
5303
5304

    .. warning::

5305
        FlashAttention uses a non-deterministic algorithm for optimal performance. To observe
5306
        deterministic behavior at the cost of performance, use FlashAttention version >= `2.4.1`
5307
5308
        and set the environment variable :attr:`NVTE_ALLOW_NONDETERMINISTIC_ALGO=0`. In order
        to disable`flash-attn` entirely, set :attr:`NVTE_FLASH_ATTN=0`.
5309

5310
5311
5312
5313
5314
5315
5316
    .. note::

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


5317
5318
5319
5320
    Parameters
    ----------
    num_attention_heads : int
                         number of attention heads in the transformer layer.
5321
5322
5323
    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.
5324
5325
5326
5327
5328
5329
5330
5331
    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`.
5332
5333
    attention_dropout: float, default = 0.0
                      dropout probability for the dropout op during multi-head attention.
5334
    attn_mask_type: str, default = `causal`
5335
                   type of attention mask passed into softmax operation, options are "`no_mask`",
5336
5337
5338
5339
5340
5341
5342
5343
5344
                   "`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
5345
                   "`padding_causal`" and "`padding_causal_bottom_right`", Transformer Engine
5346
5347
5348
5349
5350
5351
5352
5353
5354
5355
5356
5357
5358
5359
                   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].
5360
5361
5362
5363
    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
5364
5365
5366
                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
5367
                be overridden by :attr:`window_size` in `forward` as well.
5368
5369
    attention_type: str, default = `self`
                   type of attention, either "`self`" and "`cross`".
5370
5371
5372
    layer_number: int, default = `None`
                 layer number of the current `DotProductAttention` when multiple such modules
                 are concatenated, for instance in consecutive transformer blocks.
5373
5374
5375
    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,
5376
               `h` the number of heads, `d` head size, and `t` the total number of tokens
5377
5378
5379
5380
5381
               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.
5382
               For that, please use `get_qkv_layout` to gain the layout information.
5383
5384
    softmax_scale: Optional[float], default = `None`
                softmax scale for the attention scores. If `None`, defaults to
5385
                `1.0/math.sqrt(kv_channels if isinstance(kv_channels, int) else kv_channels[0])`.
5386
5387
5388
5389
5390
5391
5392
5393
5394

    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.
5395
    cp_group : Union[ProcessGroup, List[ProcessGroup]], default = `None`
5396
              context parallel process group.
5397
5398
5399
              ProcessGroup is for cp_comm_type of "p2p", "all_gather", and "a2a".
              List[ProcessGroup] is for cp_comm_type of "a2a+p2p", where cp_group[0]
              and cp_group[1] are for a2a and p2p communications respectively.
5400
5401
5402
5403
5404
5405
5406
    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.
5407
    cp_comm_type : str, default = `p2p`
5408
                  inter-gpu communication type for context parallelism.
5409
                  Can be "p2p" or "all_gather" or "a2a" or "a2a+p2p".
5410
5411
5412
5413
5414
5415
                  "p2p": Exchange KV chunks with P2P communications in ring topology.
                         P2P is async and can be overlapped with attention compute.
                  "all_gather": All-gather to get full sequence of KV before attention.
                                The all-gather is not async, and cannot be overlapped.
                  "a2a": Like DeepSpeed Ulysses, scatter attention heads across the CP
                         group, and gather to get full sequence of QKV.
5416
5417
5418
                  "a2a+p2p": hierarchical CP implementation. First applying a2a to QKV
                  across each CP sub-group (e.g., via NVLink), then exchanging KV with
                  p2p between sub-groups (e.g., via IBLink).
5419
5420
5421
5422
5423
    """

    def __init__(
        self,
        num_attention_heads: int,
5424
        kv_channels: Union[int, Tuple[int, int]],
5425
        num_gqa_groups: Optional[int] = None,
5426
        attention_dropout: float = 0.0,
5427
        qkv_format: str = "sbhd",
5428
        attn_mask_type: str = "causal",
5429
        window_size: Optional[Tuple[int, int]] = None,
5430
5431
5432
5433
5434
        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,
5435
        attention_type: str = "self",
5436
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
5437
        cp_global_ranks: List[int] = None,
5438
        cp_stream: torch.cuda.Stream = None,
5439
        cp_comm_type: str = "p2p",
5440
        softmax_scale: Optional[float] = None,
5441
5442
5443
    ) -> None:
        super().__init__()

5444
        self.logger = logging.getLogger("DotProductAttention")
5445
        self.logger.setLevel(attn_log._log_level)
5446
        if not self.logger.hasHandlers():
5447
            self.logger.addHandler(attn_log._stream_handler)
5448
        self.qkv_format = qkv_format
5449
        attn_mask_type = attn_mask_type.replace(",", "_")
5450
5451
        if attn_mask_type == "causal_padding":
            attn_mask_type = "padding_causal"
5452
        self.attn_mask_type = attn_mask_type
5453
        self.window_size = dpa_utils.check_set_window_size(attn_mask_type, window_size)
5454
5455
5456
5457
5458
5459
5460
        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)
5461
        self.get_rng_state_tracker = get_rng_state_tracker
5462
        self.num_attention_heads = num_attention_heads
5463
        self.layer_number = 1 if layer_number is None else layer_number
5464
5465
5466
        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream
5467
        self.cp_comm_type = cp_comm_type
5468

5469
5470
5471
5472
5473
5474
        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]
        )
5475

5476
        self.num_gqa_groups = num_attention_heads if num_gqa_groups is None else num_gqa_groups
5477
        self.num_gqa_groups_per_partition = int(self.num_gqa_groups // self.tp_size)
5478

5479
5480
5481
        assert (
            num_attention_heads % self.num_gqa_groups == 0
        ), "The number of attention heads must be divisible by the number of GQA groups!"
5482

5483
        self.rng_states_tracker = None
5484
5485
5486
        if sequence_parallel or get_rng_state_tracker is None:
            attention_dropout_ctx = nullcontext
        else:
5487
5488
5489
            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
5490

5491
        if softmax_scale is None:
5492
5493
5494
            softmax_scale = 1.0 / math.sqrt(
                kv_channels if isinstance(kv_channels, int) else kv_channels[0]
            )
5495

5496
5497
5498
        self.deterministic = (
            not bool(int(os.getenv("NVTE_ALLOW_NONDETERMINISTIC_ALGO", "1")))
            or torch.are_deterministic_algorithms_enabled()
5499
        )
5500
5501
5502
5503
5504
5505
5506
5507
5508
5509
5510
5511
5512
5513
5514
5515
5516
5517
5518
        # 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"
5519

5520
        assert attention_type in AttnTypes, f"attention_type {attention_type} not supported"
5521
5522
5523
5524

        self.attention_type = attention_type
        self.attention_dropout = attention_dropout

5525
5526
5527
5528
5529
        attn_kwargs = {
            "attention_dropout": attention_dropout,
            "attention_dropout_ctx": attention_dropout_ctx,
        }

5530
5531
5532
5533
5534
5535
5536
        self.flash_attention = FlashAttention(
            softmax_scale,
            attention_type=attention_type,
            layer_number=layer_number,
            deterministic=self.deterministic,
            **attn_kwargs,
        )
5537

5538
        # Instantiating three types since use of flash-attn and FusedAttention
5539
        # might be ruled out due to forward inputs.
5540
5541
5542
5543
5544
5545
5546
        self.fused_attention = FusedAttention(
            softmax_scale,
            attention_type=attention_type,
            layer_number=layer_number,
            deterministic=self.deterministic,
            **attn_kwargs,
        )
5547

5548
        self.unfused_attention = UnfusedDotProductAttention(
5549
5550
5551
5552
            softmax_scale,
            attention_type=attention_type,
            **attn_kwargs,
            layer_number=layer_number,
5553
        )
5554

5555
5556
5557
        def remove_extra_states_check(self, incompatible_keys):  # pylint: disable=unused-argument
            """
            Temporarily remove core_attention._extra_state as a missing key
5558
5559
            when loading older Transformer Engine checkpoints. Will phase out
            this hook in Transformer Engine 2.0.
5560
5561
5562
5563
5564
5565
5566
            """
            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)

5567
5568
5569
5570
5571
5572
5573
5574
5575
5576
5577
5578
5579
5580
5581
5582
5583
5584
5585
5586
5587
5588
    def _load_from_state_dict(
        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
    ):
        """
        This function helps to load Transformer Engine 1.6 and 1.7 checkpoints, where FP8 attention
        metadata is stored under the `core_attention.fused_attention._extra_state` key and not the
        `core_attention._extra_state` key. Please see `FP8 checkpoint compatibility
        <https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/faq.html#fp8-checkpoint-compatibility>`_ for more details.
        """
        fused_attn_key = False
        dot_product_attn_key = False
        for k in state_dict.keys():
            if "core_attention.fused_attention._extra_state" in k:
                fused_attn_key = True
            if "core_attention._extra_state" in k:
                dot_product_attn_key = True
        if fused_attn_key and not dot_product_attn_key:
            prefix = prefix + "fused_attention."
        super()._load_from_state_dict(
            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
        )

5589
5590
5591
5592
    def _checkpointed_attention_forward(
        self,
        attention_func: Callable,
        *forward_args: Tuple[torch.Tensor, ...],
5593
        **forward_kwargs: Dict[str, Any],
5594
5595
5596
    ) -> torch.Tensor:
        """Forward method with activation checkpointing."""

5597
5598
        def custom_forward(*input_args, **input_kwargs):
            return attention_func(*input_args, **input_kwargs)
5599
5600
5601

        hidden_states = checkpoint(
            custom_forward,
5602
5603
5604
            distribute_saved_activations=False,
            get_rng_state_tracker=self.get_rng_state_tracker,
            tp_group=self.tp_group,
5605
            *forward_args,
5606
            **forward_kwargs,
5607
5608
5609
5610
        )

        return hidden_states

5611
5612
    def set_context_parallel_group(
        self,
5613
        cp_group: Union[dist_group_type, List[dist_group_type], None],
5614
5615
        cp_global_ranks: List[int],
        cp_stream: torch.cuda.Stream,
5616
        cp_comm_type: str = "p2p",
5617
    ) -> None:
5618
5619
5620
5621
5622
5623
        """
        Set the context parallel attributes for the given
        module before executing the forward pass.

        Parameters
        ----------
5624
        cp_group : Union[ProcessGroup, List[ProcessGroup]]
5625
                  context parallel process group.
5626
5627
5628
                  ProcessGroup is for cp_comm_type of "p2p", "all_gather", and "a2a".
                  List[ProcessGroup] is for cp_comm_type of "a2a+p2p", where cp_group[0]
                  and cp_group[1] are for a2a and p2p communications respectively.
5629
5630
5631
5632
        cp_global_ranks : List[int]
                         list of global ranks in the context group.
        cp_stream : torch.cuda.Stream
                   cuda stream for context parallel execution.
5633
        cp_comm_type : str, default = `p2p`
5634
                      inter-gpu communication type for context parallelism.
5635
                      Can be "p2p" or "all_gather" or "a2a" or "a2a+p2p".
5636
5637
5638
5639
5640
5641
                      "p2p": Exchange KV chunks with P2P communications in ring topology.
                             P2P is async and can be overlapped with attention compute.
                      "all_gather": All-gather to get full sequence of KV before attention.
                                    The all-gather is not async, and cannot be overlapped.
                      "a2a": Like DeepSpeed Ulysses, scatter attention heads across the CP
                             group, and gather to get full sequence of QKV.
5642
5643
5644
                      "a2a+p2p": hierarchical CP implementation. First applying a2a to QKV
                      across each CP sub-group (e.g., via NVLink), then exchanging KV with
                      p2p between sub-groups (e.g., via IBLink).
5645
        """
5646
5647
5648
        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream
5649
        self.cp_comm_type = cp_comm_type
5650

5651
    @no_torch_dynamo(recursive=False)
5652
5653
5654
5655
5656
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
5657
5658
5659
5660
5661
5662
5663
5664
        attention_mask: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]] = None,
        qkv_format: str = None,
        cu_seqlens_q: torch.Tensor = None,
        cu_seqlens_kv: torch.Tensor = None,
        cu_seqlens_q_padded: torch.Tensor = None,
        cu_seqlens_kv_padded: torch.Tensor = None,
        max_seqlen_q: int = None,
        max_seqlen_kv: int = None,
5665
        attn_mask_type: Optional[str] = None,
5666
        window_size: Optional[Tuple[int, int]] = None,
5667
        checkpoint_core_attention: bool = False,
5668
5669
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
5670
        alibi_slopes: Optional[torch.Tensor] = None,
5671
        fast_zero_fill: bool = True,
5672
        inference_params: Optional[InferenceParams] = None,
5673
        pad_between_seqs: Optional[bool] = None,
5674
5675
5676
5677
5678
5679
    ) -> torch.Tensor:
        """
        Dot Product Attention Layer.

        .. note::

5680
5681
            Argument :attr:`attention_mask` is only used when :attr:`attn_mask_type`
            includes '"padding"' or `"arbitrary"`.
5682

5683
5684
        .. note::

5685
5686
5687
5688
5689
5690
5691
5692
5693
5694
5695
5696
5697
            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,
5698
            and FusedAttention backend if applicable, to use. Transformer Engine prioritizes
5699
5700
5701
5702
            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
5703
5704
            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
5705
            optimizations in FusedAttention. When unset, Transformer Engine determines the code path
5706
5707
            based on its internal logic. These optimizations trade memory for performance
            and should be used with care.
5708

5709
5710
5711
5712
5713
5714
5715
5716
5717
5718
5719
5720
5721
5722
5723
5724
5725
5726
5727
5728
5729
5730
5731
5732
5733
5734
5735
5736
5737
5738
5739
5740
5741
5742
5743
5744
5745
5746
5747
5748
5749
5750
5751
5752
5753
5754
5755
5756
5757
5758
5759
5760
5761
5762
        .. note::
            .. _cu_seqlens note:

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

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

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

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

        .. note::
            .. _max_seqlen note:

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

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

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

5763
5764
5765
5766
5767
5768
5769
5770
        Parameters
        ----------
        query_layer : torch.Tensor
                     Query tensor.
        key_layer : torch.Tensor
                   Key tensor.
        value_layer : torch.Tensor
                     Value tensor.
5771
5772
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]],
             default = `None`. Boolean tensor(s) used to mask out attention softmax input.
5773
             It should be `None` for causal masks and "`no_mask`". For padding masks, it should be
5774
5775
             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]
5776
5777
5778
5779
             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.
5780
5781
5782
        qkv_format: str, default = `None`
                   If provided, overrides :attr:`qkv_format` from initialization.
        cu_seqlens_q: Optional[torch.Tensor], default = `None`
5783
                   Cumulative sum of sequence lengths (without offset) in a batch for `query_layer`,
5784
                   with shape [batch_size + 1] and dtype torch.int32.
5785
                   See :ref:`note<cu_seqlens note>` for more details.
5786
        cu_seqlens_kv: Optional[torch.Tensor], default = `None`
5787
5788
                   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.
5789
                   See :ref:`note<cu_seqlens note>` for more details.
5790
5791
5792
5793
5794
        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`.
5795
                   See :ref:`note<cu_seqlens note>` for more details.
5796
5797
5798
5799
5800
        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`.
5801
                   See :ref:`note<cu_seqlens note>` for more details.
5802
5803
        max_seqlen_q: Optional[int], default = `None`
                      Maximum sequence length in `query_layer`.
5804
                      See :ref:`note<max_seqlen note>` for more details.
5805
5806
        max_seqlen_kv: Optional[int], default = `None`
                       Maximum sequence length in `key_layer` and `value_layer`.
5807
                       See :ref:`note<max_seqlen note>` for more details.
5808
5809
5810
5811
5812
5813
5814
        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.
5815
        window_size: Optional[Tuple[int, int]], default = `None`
5816
                    Sliding window size for local attention.
5817
5818
5819
5820
5821
        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.
5822
        core_attention_bias_type: str, default = `no_bias`
5823
                    Bias type, {`no_bias`, `pre_scale_bias`, `post_scale_bias`, `alibi`}
5824
        core_attention_bias: Optional[torch.Tensor], default = `None`
5825
5826
                    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.
5827
5828
5829
5830
        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.
5831
        fast_zero_fill: bool, default = `True`
5832
                    Whether to use the fast path to set output tensors to 0 or not.
5833
5834
5835
5836
5837
5838
5839
5840
5841
5842
        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.
5843
5844
5845
        pad_between_seqs: Optional[bool], default = `None`
            If None, inferred from qkv_format, cu_seqlens and cu_seqlens_padded.
            If true, there are padding tokens between individual sequences in a packed batch.
5846
        """
5847

5848
5849
5850
5851
5852
        with self.prepare_forward(
            query_layer,
            num_gemms=3,
            allow_non_contiguous=True,
        ) as query_layer:
5853
5854
5855
5856
5857
5858
5859
5860
5861
5862
            # checks for RNG
            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."

            # checks for FP8
5863
5864
5865
5866
            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
5867
                        self.logger.warning(
5868
5869
5870
                            """Forcing fp8_meta["recipe"].fp8_dpa=True due to """
                            """fp8_meta["recipe"].fp8_mha=True"""
                        )
5871
5872
5873
5874
5875
5876
5877
5878
5879
5880
            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."""
5881

5882
            # checks for q/k/v shapes
5883
5884
5885
            assert (
                query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
            ), "DotProductAttention only supports CUDA tensors."
5886
5887
5888
            assert (
                query_layer.dtype == key_layer.dtype and query_layer.dtype == value_layer.dtype
            ), "Queries, keys and values must have the same data type!"
5889
5890
5891
            assert (
                key_layer.shape[:-1] == value_layer.shape[:-1]
            ), "Keys and values must have the same batch size, sequence length and number of heads!"
5892
5893
            num_attention_heads = query_layer.shape[-2]
            num_gqa_groups = key_layer.shape[-2]
5894
            assert (
5895
5896
5897
5898
5899
5900
                query_layer.shape[-1] == key_layer.shape[-1]
            ), "Queries and keys must have the same head dimension!"
            head_dim_qk, head_dim_v = query_layer.shape[-1], value_layer.shape[-1]
            assert (
                head_dim_qk == self.hidden_size_per_attention_head_k
            ), f"Keys have head_dim = {head_dim_qk}, "
5901
5902
            "but expected head_dim = {self.hidden_size_per_attention_head_k}!"
            assert (
5903
5904
                head_dim_v == self.hidden_size_per_attention_head_v
            ), f"Values have head_dim = {head_dim_v}, "
5905
            "but expected head_dim = {self.hidden_size_per_attention_head_v}!"
5906
5907
5908
5909
            assert num_gqa_groups == self.num_gqa_groups_per_partition, (
                "Keys and values must have num_gqa_group ="
                f" {self.num_gqa_groups_per_partition} heads! Found {num_gqa_groups}."
            )
5910

5911
            # checks for attention mask
5912
5913
5914
5915
5916
5917
            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"
5918
            assert (
5919
5920
                attn_mask_type in AttnMaskTypes
            ), f"Attention mask type {attn_mask_type} is not supported!"
5921

5922
            # checks for sliding window
5923
5924
            if window_size is None:
                window_size = self.window_size
5925
            window_size = dpa_utils.check_set_window_size(attn_mask_type, window_size)
5926

5927
5928
5929
            # checks for qkv_format
            if qkv_format is None:
                qkv_format = self.qkv_format
5930
5931
5932
5933
5934
            assert qkv_format in [
                "sbhd",
                "bshd",
                "thd",
            ], "DotProductAttention only supports qkv_format = {'sbhd', 'bshd', 'thd'}!"
5935
5936
5937
5938
5939
5940
5941
5942
5943
5944
5945
5946
5947
            batch_size = None
            if qkv_format in ["sbhd", "bshd"]:
                assert all(
                    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=}!"
                if qkv_format == "sbhd":
                    batch_size = query_layer.shape[1]
                    max_seqlen_q = query_layer.shape[0] if max_seqlen_q is None else max_seqlen_q
                    max_seqlen_kv = key_layer.shape[0] if max_seqlen_kv is None else max_seqlen_kv
                else:
                    batch_size = query_layer.shape[0]
                    max_seqlen_q = query_layer.shape[1] if max_seqlen_q is None else max_seqlen_q
                    max_seqlen_kv = key_layer.shape[1] if max_seqlen_kv is None else max_seqlen_kv
5948
            if qkv_format == "thd":
5949
                assert all(
5950
5951
                    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!"
5952
5953
5954
                assert (
                    "padding" in attn_mask_type
                ), "Attention mask type must be padding or padding_causal for qkv_format=thd!"
5955
5956
5957
5958
5959
5960
5961
5962
5963
5964
5965
                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!"
5966
                batch_size = len(cu_seqlens_q) - 1
5967
                if max_seqlen_q is None:
5968
5969
5970
5971
                    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]
5972
                    max_seqlen_q = int((seqlens_q.max().item() + 63) // 64 * 64)
5973
                if max_seqlen_kv is None:
5974
5975
5976
5977
                    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]
5978
                    max_seqlen_kv = int((seqlens_kv.max().item() + 63) // 64 * 64)
5979

5980
5981
5982
5983
5984
5985
5986
5987
5988
5989
5990
5991
5992
5993
5994
5995
5996
5997
5998
5999
6000
6001
6002
6003
6004
6005
6006
6007
6008
6009
6010
6011
6012
6013
6014
6015
6016
6017
6018
6019
6020
6021
6022
6023
6024
6025
6026
6027
6028
6029
6030
6031
6032
6033
6034
6035
6036
6037
6038
6039
6040
6041
6042
6043
6044
6045
6046
6047
6048
6049
            # update KV cache and retrieve saved tokens from cache for inference
            if inference_params is not None:
                assert self.layer_number is not None, "Layer number must be set!"

                # convert top-left causal to bottom-right causal due to KV caching
                # users can still use the same attention mask for inference as for training
                assert "padding" in attn_mask_type, "KV caching requires padding mask!"
                if attn_mask_type == "padding_causal":
                    attn_mask_type = attn_mask_type + "_bottom_right"

                self.attention_type = "cross"
                self.flash_attention.attention_type = self.attention_type
                self.fused_attention.attention_type = self.attention_type
                self.unfused_attention.attention_type = self.attention_type

                query_layer, key_layer, value_layer = [
                    x.contiguous() if not x.is_contiguous() else x
                    for x in [query_layer, key_layer, value_layer]
                ]

                # get full K/V tensors from cache and adjust cu_seqlens, qkv_format based on the cache
                (
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_kv,
                    qkv_format,
                ) = inference_params.step(
                    self.layer_number,
                    key_layer,
                    value_layer,
                    qkv_format,
                )
                cu_seqlens_q_padded = None
                cu_seqlens_kv_padded = None

            # get qkv's memory layout
            if all(isinstance(x, Float8Tensor) for x in [query_layer, key_layer, value_layer]):
                (
                    qkv_layout,
                    query_layer._data,
                    key_layer._data,
                    value_layer._data,
                    q_format,
                    kv_format,
                ) = dpa_utils.get_qkv_layout(
                    query_layer._data,
                    key_layer._data,
                    value_layer._data,
                    qkv_format=qkv_format,
                    inference_params=inference_params,
                )
            else:
                (
                    qkv_layout,
                    query_layer,
                    key_layer,
                    value_layer,
                    q_format,
                    kv_format,
                ) = dpa_utils.get_qkv_layout(
                    query_layer,
                    key_layer,
                    value_layer,
                    qkv_format=qkv_format,
                    inference_params=inference_params,
                )

            # adjust max_seqlen and cu_seqlens for CP
6050
6051
6052
6053
6054
6055
            cp_size = 1
            if isinstance(self.cp_group, dist_group_type):
                cp_size = get_distributed_world_size(self.cp_group)
            elif isinstance(self.cp_group, list):
                for group in self.cp_group:
                    cp_size *= get_distributed_world_size(group)
6056
            context_parallel = cp_size > 1
6057
            if q_format in ["sbhd", "bshd"]:
6058
                max_seqlen_q *= cp_size
6059
                if cu_seqlens_q is None:
6060
6061
6062
6063
                    if "padding" in attn_mask_type:
                        assert (
                            attention_mask is not None
                        ), "Please provide attention_mask for padding!"
6064
                        if self.attention_type == "self":
6065
                            cu_seqlens_q = dpa_utils.get_cu_seqlens(attention_mask)
6066
                        else:
6067
                            cu_seqlens_q = dpa_utils.get_cu_seqlens(attention_mask[0])
6068
                    else:
6069
                        cu_seqlens_q = dpa_utils.get_full_cu_seqlens(
6070
6071
6072
6073
                            batch_size,
                            max_seqlen_q,
                            query_layer.device,
                        )
6074
6075
6076
6077
6078
6079
6080
6081
6082
6083
6084
6085
            if kv_format in ["sbhd", "bshd"]:
                max_seqlen_kv *= cp_size
                if cu_seqlens_kv is None:
                    if "padding" in attn_mask_type:
                        assert (
                            attention_mask is not None
                        ), "Please provide attention_mask for padding!"
                        if self.attention_type == "self":
                            cu_seqlens_kv = dpa_utils.get_cu_seqlens(attention_mask)
                        else:
                            cu_seqlens_kv = dpa_utils.get_cu_seqlens(attention_mask[1])
                    else:
6086
                        cu_seqlens_kv = dpa_utils.get_full_cu_seqlens(
6087
6088
6089
6090
                            batch_size,
                            max_seqlen_kv,
                            key_layer.device,
                        )
6091

6092
            # set ALiBi attributes
6093
6094
6095
6096
6097
6098
6099
6100
            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
6101
            bottom_right_alignment = (attn_mask_type not in ["causal", "padding_causal"],)
6102
6103
6104
6105
6106
6107
6108
6109
            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
6110
                    or _alibi_cache["_bottom_right_alignment"] != bottom_right_alignment
6111
6112
6113
6114
6115
                    or _alibi_cache["_alibi_slopes"] is None
                ):
                    _alibi_cache["_alibi_slopes_require_update"] = True
                    _alibi_cache["_alibi_bias_require_update"] = True

6116
            # detect bias shape
6117
6118
            core_attention_bias_shape = None
            if core_attention_bias is not None:
6119
                if (
6120
6121
                    core_attention_bias.shape[0] == batch_size
                    and core_attention_bias.shape[1] == query_layer.shape[-2]
6122
                ):
6123
6124
6125
6126
6127
6128
6129
6130
6131
6132
6133
6134
6135
6136
6137
6138
6139
                    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"

6140
6141
6142
6143
6144
6145
6146
6147
6148
6149
6150
            if pad_between_seqs is None:
                if qkv_format == "thd":
                    pad_between_seqs = (
                        cu_seqlens_q_padded is not None
                        and not torch.equal(cu_seqlens_q_padded[:-1], cu_seqlens_q[:-1])
                    ) or (
                        cu_seqlens_kv_padded is not None
                        and not torch.equal(cu_seqlens_kv_padded[:-1], cu_seqlens_kv[:-1])
                    )
                else:
                    pad_between_seqs = False
6151

6152
            # gather attention params for get_attention_backend
6153
            attention_params = dpa_utils.AttentionParams(
6154
6155
6156
6157
                qkv_type=type(query_layer),
                qkv_dtype=query_layer.dtype,
                qkv_layout=qkv_layout,
                batch_size=batch_size,
6158
6159
                num_heads=num_attention_heads,
                num_gqa_groups=num_gqa_groups,
6160
6161
                max_seqlen_q=max_seqlen_q,
                max_seqlen_kv=max_seqlen_kv,
6162
6163
                head_dim_qk=head_dim_qk,
                head_dim_v=head_dim_v,
6164
6165
6166
6167
6168
6169
6170
6171
6172
6173
6174
                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,
6175
6176
                deterministic=self.deterministic,
                is_training=self.training,
6177
6178
                fp8=self.fp8,
                fp8_meta=self.fp8_meta,
6179
                inference_params=inference_params,
6180
            )
6181
            global _attention_backends
6182
6183
6184
6185
6186
6187
6188
6189
6190
            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,
6191
                    flash_attention_backend,
6192
6193
6194
6195
                    use_fused_attention,
                    fused_attention_backend,
                    use_unfused_attention,
                    _,
6196
6197
6198
6199
                ) = dpa_utils.get_attention_backend(attention_params)
                # Set global _attention_backends var using return value
                # from get_attention_backend()
                _attention_backends["use_flash_attention"] = use_flash_attention
6200
                _attention_backends["flash_attention_backend"] = flash_attention_backend
6201
6202
6203
6204
                _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
6205
                if use_flash_attention:
6206
6207
                    self.logger.info(
                        "Running with FlashAttention backend (version %s)",
6208
                        flash_attention_backend,
6209
                    )
6210
6211
6212
6213
                elif use_fused_attention:
                    self.logger.info(
                        "Running with FusedAttention backend (sub-backend %s)",
                        int(fused_attention_backend),
6214
                    )
6215
6216
6217
6218
                elif use_unfused_attention:
                    self.logger.info("Running with UnfusedDotProductAttention backend")
            else:
                use_flash_attention = _attention_backends["use_flash_attention"]
6219
                flash_attention_backend = _attention_backends["flash_attention_backend"]
6220
6221
6222
                use_fused_attention = _attention_backends["use_fused_attention"]
                fused_attention_backend = _attention_backends["fused_attention_backend"]
                use_unfused_attention = _attention_backends["use_unfused_attention"]
6223

6224
6225
            # raise exception if no backend is available
            if sum([use_flash_attention, use_fused_attention, use_unfused_attention]) == 0:
6226
6227
6228
6229
6230
                raise ValueError(
                    "No dot product attention backend is available for the provided inputs. Please"
                    " run with NVTE_DEBUG=1 NVTE_DEBUG_LEVEL=2 to find out the reasons for"
                    " disabling all backends."
                )
6231
6232

            # run attention
6233
6234
            if use_flash_attention:
                if core_attention_bias_type == "alibi":
6235
6236
                    alibi_slopes, _ = dpa_utils.get_alibi(
                        _alibi_cache,
6237
6238
6239
6240
6241
6242
6243
6244
6245
6246
6247
6248
6249
6250
6251
6252
6253
6254
6255
                        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,
6256
                    cp_comm_type=self.cp_comm_type,
6257
6258
                    max_seqlen_q=max_seqlen_q,
                    max_seqlen_kv=max_seqlen_kv,
6259
6260
                    fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                    fp8_meta=self.fp8_meta,
6261
                    quantizers=self.quantizers,
6262
6263
                    inference_params=inference_params,
                    flash_attention_backend=flash_attention_backend,
6264
                )
6265

6266
            if use_fused_attention:
6267
6268
                fu_core_attention_bias_type = core_attention_bias_type
                fu_core_attention_bias = core_attention_bias
6269
6270
6271
                if core_attention_bias_type == "alibi" and (
                    alibi_slopes is not None or max_seqlen_q != max_seqlen_kv
                ):
6272
                    fu_core_attention_bias_type = "post_scale_bias"
6273
6274
                    _, fu_core_attention_bias = dpa_utils.get_alibi(
                        _alibi_cache,
6275
6276
6277
6278
6279
                        query_layer.shape[-2],
                        max_seqlen_q,
                        max_seqlen_kv,
                        alibi_slopes=alibi_slopes,
                        bias_dtype=query_layer.dtype,
6280
                        bottom_right_alignment=attn_mask_type not in ["causal", "padding_causal"],
6281
                    )
6282
                # checkpoint_core_attention=False
6283
6284
6285
6286
6287
6288
6289
6290
6291
                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,
6292
6293
                        cu_seqlens_q_padded=cu_seqlens_q_padded,
                        cu_seqlens_kv_padded=cu_seqlens_kv_padded,
6294
6295
6296
6297
                        max_seqlen_q=max_seqlen_q,
                        max_seqlen_kv=max_seqlen_kv,
                        attn_mask_type=attn_mask_type,
                        attention_mask=attention_mask,
6298
                        window_size=window_size,
6299
6300
6301
6302
6303
6304
6305
                        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,
6306
                        cp_comm_type=self.cp_comm_type,
6307
6308
                        fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                        fp8_meta=self.fp8_meta,
6309
                        quantizers=self.quantizers,
6310
                        pad_between_seqs=pad_between_seqs,
6311
                        inference_params=inference_params,
6312
6313
                    )
                return self.fused_attention(
6314
6315
6316
6317
6318
6319
                    query_layer,
                    key_layer,
                    value_layer,
                    qkv_layout=qkv_layout,
                    cu_seqlens_q=cu_seqlens_q,
                    cu_seqlens_kv=cu_seqlens_kv,
6320
6321
                    cu_seqlens_q_padded=cu_seqlens_q_padded,
                    cu_seqlens_kv_padded=cu_seqlens_kv_padded,
6322
6323
                    max_seqlen_q=max_seqlen_q,
                    max_seqlen_kv=max_seqlen_kv,
6324
6325
                    attn_mask_type=attn_mask_type,
                    attention_mask=attention_mask,
6326
                    window_size=window_size,
6327
                    fused_attention_backend=fused_attention_backend,
6328
6329
                    core_attention_bias_type=fu_core_attention_bias_type,
                    core_attention_bias=fu_core_attention_bias,
6330
6331
6332
6333
                    fast_zero_fill=fast_zero_fill,
                    cp_group=self.cp_group,
                    cp_global_ranks=self.cp_global_ranks,
                    cp_stream=self.cp_stream,
6334
                    cp_comm_type=self.cp_comm_type,
6335
6336
                    fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                    fp8_meta=self.fp8_meta,
6337
                    quantizers=self.quantizers,
6338
                    pad_between_seqs=pad_between_seqs,
6339
                    inference_params=inference_params,
6340
                )
6341

6342
            from .cpu_offload import CPUOffloadEnabled
6343

6344
6345
6346
6347
6348
            if CPUOffloadEnabled:
                warnings.warn(
                    "Attention activation Offloading is only implemented"
                    "with Flash Attention and Fused Attention!"
                )
6349

6350
6351
6352
6353
6354
6355
6356
6357
6358
6359
6360
6361
            if use_unfused_attention:
                if checkpoint_core_attention:
                    return self._checkpointed_attention_forward(
                        self.unfused_attention,
                        query_layer,
                        key_layer,
                        value_layer,
                        qkv_layout=qkv_layout,
                        cu_seqlens_q=cu_seqlens_q,
                        cu_seqlens_kv=cu_seqlens_kv,
                        attn_mask_type=attn_mask_type,
                        attention_mask=attention_mask,
6362
                        window_size=window_size,
6363
6364
6365
                        core_attention_bias_type=core_attention_bias_type,
                        core_attention_bias=core_attention_bias,
                        alibi_slopes=alibi_slopes,
6366
                        inference_params=inference_params,
6367
6368
                    )
                return self.unfused_attention(
6369
6370
6371
                    query_layer,
                    key_layer,
                    value_layer,
6372
6373
6374
6375
6376
                    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,
6377
                    window_size=window_size,
6378
6379
6380
                    core_attention_bias_type=core_attention_bias_type,
                    core_attention_bias=core_attention_bias,
                    alibi_slopes=alibi_slopes,
6381
                    inference_params=inference_params,
6382
                )
6383
            return None
6384
6385


6386
6387
6388
6389
6390
6391
6392
class MultiheadAttention(torch.nn.Module):
    r"""
    Multi-head Attention (MHA), including Query,
    Key, Value and Output projection.

    .. note::

6393
6394
        Argument :attr:`attention_mask` in the `forward` call is only used when
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
6395

6396
6397
6398
6399
6400
6401
6402
6403
6404
6405
6406
6407
6408
6409
6410
6411
6412
6413
6414
6415
6416
6417
6418
6419
6420
    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.
6421
6422
    attn_mask_type: {'no_mask', 'padding', 'causal', 'padding_causal', 'causal_bottom_right',
                   'padding_causal_bottom_right','arbitrary'},
6423
                   default = `causal`
6424
6425
6426
6427
6428
                   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.
6429
6430
6431
6432
    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
6433
6434
6435
                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
6436
                be overridden by :attr:`window_size` in `forward` as well.
6437
6438
6439
6440
6441
6442
6443
6444
6445
6446
6447
6448
6449
    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.
6450
6451
    input_layernorm: bool, default = `False`
                     if set to `True`, layer normalization to the input is applied.
6452
6453
6454
6455
6456
6457
6458
6459
6460
6461
6462
6463
6464
6465
6466
6467
6468
6469
6470
6471
    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"
6472
          The device on which the parameters of the model will be allocated. It is the user's
6473
6474
          responsibility to ensure all parameters are moved to the GPU before running the
          forward pass.
6475
6476
6477
6478
6479
6480
6481
    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.
6482
            For that, please use `get_qkv_layout` to gain the layout information.
6483
6484
6485
6486
6487
6488
6489
6490
6491
6492
6493
6494
6495
6496
6497
6498
6499
6500
6501
6502
6503
6504
6505
6506
6507
6508
6509
6510
6511
6512
6513
6514
6515
6516
6517
6518
6519
6520
6521
6522

    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`.
6523
6524
6525
6526
6527
6528
    """

    def __init__(
        self,
        hidden_size: int,
        num_attention_heads: int,
6529
6530
6531
6532
6533
        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,
6534
        layer_number: Optional[int] = None,
6535
        attn_mask_type: str = "causal",
6536
        window_size: Optional[Tuple[int, int]] = None,
6537
6538
        tp_group: Optional[dist_group_type] = None,
        tp_size: int = 1,
6539
        num_gqa_groups: Optional[int] = None,
6540
6541
6542
        fuse_wgrad_accumulation: bool = False,
        get_rng_state_tracker: Optional[Callable] = None,
        sequence_parallel: bool = False,
6543
        params_dtype: Optional[torch.dtype] = None,
6544
        return_bias: bool = False,
6545
6546
6547
6548
6549
6550
6551
        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,
6552
        ub_overlap_ag: bool = False,
6553
6554
6555
6556
        ub_overlap_rs: bool = False,
        ub_overlap_rs_dgrad: bool = False,
        ub_bulk_dgrad: bool = False,
        ub_bulk_wgrad: bool = False,
6557
        bias: bool = True,
6558
        normalization: str = "LayerNorm",
6559
        device: Union[torch.device, str] = "cuda",
6560
        qkv_format: str = "sbhd",
6561
6562
    ) -> None:
        super().__init__()
6563

6564
        self.qkv_format = qkv_format
6565
        self.attn_mask_type = attn_mask_type
6566
        self.window_size = dpa_utils.check_set_window_size(attn_mask_type, window_size)
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        self.layer_number = 1 if layer_number is None else layer_number
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        self.input_layernorm = input_layernorm
        self.attention_type = attention_type
        self.get_rng_state_tracker = get_rng_state_tracker
        self.tp_group = tp_group
        self.return_layernorm_output = return_layernorm_output
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        self.params_dtype = torch.get_default_dtype() if params_dtype is None else params_dtype
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        self.num_attention_heads = num_attention_heads
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        self.return_bias = return_bias
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        self.cp_size = 1
        self.cp_rank = 0
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        kv_channels = kv_channels if kv_channels else (hidden_size // num_attention_heads)

        if init_method is None:
            init_method = get_default_init_method()
        if output_layer_init_method is None:
            output_layer_init_method = get_default_init_method()
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        if not fuse_qkv_params:
            qkv_weight_interleaved = False
        self.qkv_weight_interleaved = qkv_weight_interleaved

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

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

        qkv_parallel_mode = "column" if set_parallel_mode else None

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

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

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

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

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

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

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

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        # Deep iterate but skip self to avoid infinite recursion.
        for index, child in enumerate(self.modules()):
            if index == 0:
                continue
            if hasattr(child, "set_context_parallel_group"):
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                child.set_context_parallel_group(cp_group, cp_global_ranks, cp_stream, cp_comm_type)
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    def forward(
        self,
        hidden_states: torch.Tensor,
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        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
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        encoder_output: Optional[torch.Tensor] = None,
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        attn_mask_type: Optional[str] = None,
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        window_size: Optional[Tuple[int, int]] = None,
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        is_first_microbatch: Optional[bool] = None,
        checkpoint_core_attention: bool = False,
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        inference_params: Optional[InferenceParams] = None,
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        rotary_pos_emb: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
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        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
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        alibi_slopes: Optional[torch.Tensor] = None,
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        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
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        fast_zero_fill: bool = True,
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        pad_between_seqs: Optional[bool] = None,
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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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        pad_between_seqs: Optional[bool], default = `None`
            If None, inferred from qkv_format, cu_seqlens and cu_seqlens_padded.
            If true, there are padding tokens between individual sequences in a packed batch.
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        """
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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 = dpa_utils.check_set_window_size(attn_mask_type, window_size)
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        if "padding" in attn_mask_type and attention_mask is not None:
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            for mask in attention_mask:
                assert mask.dtype == torch.bool, "Attention mask must be in boolean type!"
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        assert (
            core_attention_bias_type in AttnBiasTypes
        ), f"core_attention_bias_type {core_attention_bias_type} is not supported!"
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        # =================================================
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        # Pre-allocate memory for key-value cache for inference
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        # =================================================

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        if (
            inference_params is not None
            and self.layer_number not in inference_params.cache_manager.cache
        ):
            inference_params.allocate_memory(self.layer_number)
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        # ======================
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        # Query, Key, and Value
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        # ======================
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        fp8_mha = (
            FP8GlobalStateManager.is_fp8_enabled()
            and FP8GlobalStateManager.get_fp8_recipe().fp8_mha
        )

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

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

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

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

            mixed_kv_layer = mixed_kv_layer.view(*new_tensor_shape)

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            # mixed_kv_layer --> 2 [sk, b, ng, hn]
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            key_layer, value_layer = _SplitAlongDim.apply(
                mixed_kv_layer,
                split_dim,
                mixed_kv_layer.shape[split_dim] // 2,
            )
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            key_layer, value_layer = (
                x.reshape(
                    x.size(0),
                    x.size(1),
                    -1,
                    self.hidden_size_per_attention_head,
                )
                for x in (key_layer, value_layer)
            )
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            # Attention head [sq, b, h] --> [sq, b, hp]
            if self.input_layernorm:
                layernorm_query_outputs = self.layernorm_query(
                    hidden_states,
                    is_first_microbatch=is_first_microbatch,
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                    fp8_output=fp8_mha and rotary_pos_emb is None,
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                )
                if self.return_layernorm_output:
                    query_layer, layernorm_output = layernorm_query_outputs
                else:
                    query_layer = layernorm_query_outputs
            else:
                query_layer = self.query_layer(
                    hidden_states,
                    is_first_microbatch=is_first_microbatch,
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                )

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

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

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

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

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

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        # ===================
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        # Output. [sq, b, h]
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        # ===================
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        projection_output = self.proj(
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            context_layer,
            is_first_microbatch=is_first_microbatch,
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            fp8_grad=isinstance(context_layer, QuantizedTensor),
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        )

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

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