attention.py 327 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.debug.pytorch.debug_state import TEDebugState
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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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from .cpu_offload import set_offloading_param
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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):
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    """
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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,
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    ):
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        # 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"))
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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,
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        ) = 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")):
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                        q = QKV_quantizer(q_f16)._data
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                    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()
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                    S_quantizer_per_step[i].amax = amax_per_step[0][i].reshape((1,))
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                    O_CP_quantizer_per_step[i] = O_CP_quantizer.copy()
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                    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:
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            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_before_attn(cp_size_a2a, q.device)
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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")):
712
                q_f16 = q
713
                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!"
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        if causal:
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            if qkv_format == "bshd":
                # [b, s, np, hn] -> [b, 2, s//2, np, hn]
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                q, k, v = [x.view(x.shape[0], 2, x.shape[1] // 2, *x.shape[2:]) for x in [q, k, v]]
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            elif qkv_format == "sbhd":
                # [s, b, np, hn] -> [2, s//2, b, np, hn]
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                q, k, v = [x.view(2, x.shape[0] // 2, *x.shape[1:]) for x in [q, k, v]]
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        if attn_bias is not None:
726
            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)
744
            )
745
        assert q.shape[-1] % 8 == 0, "hidden size per attention head should be multiple of 8"
746

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751
        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:
752
                softmax_lse_in_packed_format = fa_utils.v2_6_0_plus or use_flash_attn_3
753

754
        flash_attn_fwd = None
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        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
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                if fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus:
770
                    fa_forward_kwargs["window_size"] = (-1, 0) if causal else (-1, -1)
771
                elif fa_utils.v2_7_0_plus:
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                    fa_forward_kwargs["window_size_left"] = -1
                    fa_forward_kwargs["window_size_right"] = 0 if causal else -1
774
                if fa_utils.v2_4_plus:
775
                    fa_forward_kwargs["alibi_slopes"] = None
776
                if fa_utils.v2_5_7_plus and qkv_format == "thd":
777
                    fa_forward_kwargs["block_table"] = None
778
                if fa_utils.v2_6_0_plus:
779
                    fa_forward_kwargs["softcap"] = 0.0
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        # Flash Attn inputs
        q_inputs = [None, None]
        kv_inputs = [None, None]
784
        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)]
789
        attn_biases = [None for _ in range(cp_size)]
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        # 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)]
797
        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 = [[], []]

803
        out = None
804
        for i in range(cp_size + 1):
805
            if i < cp_size:
806
                with torch.cuda.stream(flash_attn_streams[i % 2]):
807
                    # wait until KV is received
808
                    for req in send_recv_reqs[(i + 1) % 2]:
809
810
                        req.wait()

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822
                    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,
                        )

823
                    if not fp8 or is_input_fp8 or int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
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                        kv_inputs[i % 2] = p2p_comm_buffers[i]
                    else:
                        # KV exchange is in BF16/FP16, cast received KV in each step
827
                        kv_inputs[i % 2] = QKV_quantizer(p2p_comm_buffers[i])._data
828
829
                    if causal:
                        if i == 0:
830
                            if pad_between_seqs:
831
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836
                                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
                                )
837
838
                            elif qkv_format == "thd":
                                cu_seqlens_q_per_step[i] = cu_seqlens_q // cp_size
839
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
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842
                            else:
                                cu_seqlens_q_per_step[i] = cu_seqlens_q
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv
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                            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
859
                            if use_fused_attention:
860
861
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
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867
                                    attn_bias_inputs[i % 2] = torch.cat(
                                        (
                                            attn_bias[..., idx, :],
                                            attn_bias[..., (2 * cp_size - idx - 1), :],
                                        ),
                                        dim=-1,
868
                                    ).contiguous()
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880

                                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]
                                )
881
                                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]
894

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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],
901
902
903
904
905
                                    q_part,
                                    k_part,
                                    v_part,
                                    fake_dtype=qkv_dtype,
                                    fused_attention_backend=fused_attn_backend,
906
907
908
909
910
911
912
913
914
                                    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,
915
                                )
916
917
918
919
920
                                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
921
                            else:
922
923
924
925
926
927
928
929
930
                                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,
                                )
931
                                fa_outputs = flash_attn_fwd(
932
                                    q_inputs[i % 2],
933
934
935
936
937
938
939
940
941
942
943
                                    (
                                        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,
944
                                    causal=True,
945
                                    **fa_forward_kwargs,
946
                                )
947
                                if not fa_utils.v2_7_0_plus:
948
949
                                    out_per_step[i] = fa_outputs[4]
                                    softmax_lse_per_step[i] = fa_outputs[5]
950
                                    if not use_flash_attn_3:
951
952
953
954
                                        rng_states[i] = fa_outputs[7]
                                else:
                                    out_per_step[i] = fa_outputs[0]
                                    softmax_lse_per_step[i] = fa_outputs[1]
955
                                    if not use_flash_attn_3:
956
                                        rng_states[i] = fa_outputs[3]
957
                        elif i <= rank:
958
                            if pad_between_seqs:
959
960
961
962
963
964
965
966
967
968
969
                                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,
                                )
970
971
                            elif qkv_format == "thd":
                                cu_seqlens_q_per_step[i] = cu_seqlens_q // cp_size
972
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // (cp_size * 2)
973
974
975
                            else:
                                cu_seqlens_q_per_step[i] = cu_seqlens_q
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv_half
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
                            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
                                )
992
                            if use_fused_attention:
993
                                kv_inputs[i % 2] = kv_inputs[i % 2].contiguous()
994
995
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
996
                                    attn_bias_inputs[i % 2] = attn_bias[..., idx, :].contiguous()
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008

                                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]
                                )
1009
                                fp8_meta_kwargs = {}
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
                                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
                                    )
1020
1021
                                    fp8_meta_kwargs["s_quantizer"] = S_quantizer_per_step[i]
                                    fp8_meta_kwargs["o_quantizer"] = O_CP_quantizer_per_step[i]
1022
1023
1024
1025
1026
1027
                                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],
1028
1029
1030
1031
                                    q_part,
                                    k_part,
                                    v_part,
                                    qkv_dtype,
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
                                    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,
1046
                                )
1047
1048
1049
1050
1051
                                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
1052
                            else:
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
                                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 (
1063
                                    fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus
1064
                                ):
1065
                                    fa_forward_kwargs["window_size"] = (-1, -1)
1066
                                elif fa_utils.v2_7_0_plus:
1067
1068
                                    fa_forward_kwargs["window_size_left"] = -1
                                    fa_forward_kwargs["window_size_right"] = -1
1069
                                fa_outputs = flash_attn_fwd(
1070
                                    q_inputs[i % 2],
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
                                    (
                                        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,
1082
                                    causal=False,
1083
                                    **fa_forward_kwargs,
1084
                                )
1085
                                if not fa_utils.v2_7_0_plus:
1086
1087
                                    out_per_step[i] = fa_outputs[4]
                                    softmax_lse_per_step[i] = fa_outputs[5]
1088
                                    if not use_flash_attn_3:
1089
1090
1091
1092
                                        rng_states[i] = fa_outputs[7]
                                else:
                                    out_per_step[i] = fa_outputs[0]
                                    softmax_lse_per_step[i] = fa_outputs[1]
1093
                                    if not use_flash_attn_3:
1094
                                        rng_states[i] = fa_outputs[3]
1095
                        else:
1096
                            if pad_between_seqs:
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
                                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,
                                )
1108
1109
                            elif qkv_format == "thd":
                                cu_seqlens_q_per_step[i] = cu_seqlens_q // (cp_size * 2)
1110
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
1111
1112
1113
                            else:
                                cu_seqlens_q_per_step[i] = cu_seqlens_q_half
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
                            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
                                )
1133
                            if use_fused_attention:
1134
                                q_inputs[i % 2] = q_inputs[i % 2].contiguous()
1135
1136
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
1137
1138
1139
1140
1141
1142
                                    attn_bias_inputs[i % 2] = torch.cat(
                                        (
                                            attn_bias_[..., 1, :, idx, :],
                                            attn_bias_[..., 1, :, (2 * cp_size - idx - 1), :],
                                        ),
                                        dim=-1,
1143
                                    ).contiguous()
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155

                                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]
                                )
1156
                                fp8_meta_kwargs = {}
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
                                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
                                    )
1167
1168
                                    fp8_meta_kwargs["s_quantizer"] = S_quantizer_per_step[i]
                                    fp8_meta_kwargs["o_quantizer"] = O_CP_quantizer_per_step[i]
1169
1170
1171
1172
1173
1174
                                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],
1175
1176
1177
1178
                                    q_part,
                                    k_part,
                                    v_part,
                                    qkv_dtype,
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
                                    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,
1193
                                )
1194
1195
1196
1197
1198
                                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
1199
                            else:
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
                                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 (
1210
                                    fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus
1211
                                ):
1212
                                    fa_forward_kwargs["window_size"] = (-1, -1)
1213
                                elif fa_utils.v2_7_0_plus:
1214
1215
                                    fa_forward_kwargs["window_size_left"] = -1
                                    fa_forward_kwargs["window_size_right"] = -1
1216
                                fa_outputs = flash_attn_fwd(
1217
                                    q_inputs[i % 2],
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
                                    (
                                        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,
1229
                                    causal=False,
1230
                                    **fa_forward_kwargs,
1231
                                )
1232
                                if not fa_utils.v2_7_0_plus:
1233
1234
                                    out_per_step[i] = fa_outputs[4]
                                    softmax_lse_per_step[i] = fa_outputs[5]
1235
                                    if not use_flash_attn_3:
1236
1237
1238
1239
                                        rng_states[i] = fa_outputs[7]
                                else:
                                    out_per_step[i] = fa_outputs[0]
                                    softmax_lse_per_step[i] = fa_outputs[1]
1240
                                    if not use_flash_attn_3:
1241
                                        rng_states[i] = fa_outputs[3]
1242
                    else:
1243
                        if pad_between_seqs:
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
                            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,
                            )
1255
1256
                        elif qkv_format == "thd":
                            cu_seqlens_q_per_step[i] = cu_seqlens_q // cp_size
1257
                            cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
1258
1259
1260
                        else:
                            cu_seqlens_q_per_step[i] = cu_seqlens_q
                            cu_seqlens_kv_per_step[i] = cu_seqlens_kv
1261
                        if use_fused_attention:
1262
1263
                            if attn_bias is not None:
                                idx = (rank - i) % cp_size
1264
1265
1266
1267
1268
1269
                                attn_bias_inputs[i % 2] = torch.cat(
                                    (
                                        attn_bias[..., idx, :],
                                        attn_bias[..., (2 * cp_size - idx - 1), :],
                                    ),
                                    dim=-1,
1270
                                ).contiguous()
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282

                            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]
                            )
1283
                            fp8_meta_kwargs = {}
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
                            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
                                )
1294
1295
                                fp8_meta_kwargs["s_quantizer"] = S_quantizer_per_step[i]
                                fp8_meta_kwargs["o_quantizer"] = O_CP_quantizer_per_step[i]
1296
1297
1298
1299
1300
1301
                            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],
1302
1303
1304
1305
                                q_part,
                                k_part,
                                v_part,
                                qkv_dtype,
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
                                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,
1316
                            )
1317
1318
1319
1320
1321
                            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
1322
                        else:
1323
1324
1325
1326
1327
1328
1329
1330
1331
                            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,
                            )
1332
                            fa_outputs = flash_attn_fwd(
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
                                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,
1345
                                causal=False,
1346
                                **fa_forward_kwargs,
1347
                            )
1348
                            if not fa_utils.v2_7_0_plus:
1349
1350
                                out_per_step[i] = fa_outputs[4]
                                softmax_lse_per_step[i] = fa_outputs[5]
1351
                                if not use_flash_attn_3:
1352
1353
1354
1355
                                    rng_states[i] = fa_outputs[7]
                            else:
                                out_per_step[i] = fa_outputs[0]
                                softmax_lse_per_step[i] = fa_outputs[1]
1356
                                if not use_flash_attn_3:
1357
                                    rng_states[i] = fa_outputs[3]
1358
1359
1360
1361

            if i > 0:
                # wait until fwd restuls correction of last step is done
                if i > 1:
1362
                    flash_attn_streams[(i - 1) % 2].wait_event(fwd_results_correction_done)
1363

1364
                with torch.cuda.stream(flash_attn_streams[(i - 1) % 2]):
1365
1366
1367
1368
1369
1370
1371
1372
                    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()
                            )
1373
                    if fp8:
1374
                        out_per_step[i - 1] = out_per_step[i - 1].dequantize(dtype=torch.float32)
1375
1376
                    if i == 1:
                        softmax_lse = torch.clone(softmax_lse_per_step[0]).to(torch.double)
1377
1378
                        if qkv_format == "thd":
                            out = torch.zeros_like(q if not fp8 else out_per_step[0]).view(q.shape)
1379
1380
1381
1382
                    elif (i - 1) <= rank or not causal:
                        flash_attn_fwd_softmax_lse_correction(
                            softmax_lse, softmax_lse_per_step[i - 1]
                        )
1383
                    else:
1384
                        if qkv_format == "thd":
1385
                            tex.thd_second_half_lse_correction(
1386
1387
1388
                                softmax_lse,
                                softmax_lse_per_step[i - 1],
                                cu_seqlens_q_padded,
1389
                                softmax_lse_in_packed_format,
1390
                            )
1391
                        else:
1392
1393
1394
                            flash_attn_fwd_second_half_softmax_lse_correction(
                                softmax_lse.view(*softmax_lse.shape[:-1], 2, -1),
                                softmax_lse_per_step[i - 1],
1395
                            )
1396
1397

                if i < cp_size:
1398
                    flash_attn_streams[(i - 1) % 2].record_event(fwd_results_correction_done)
1399
1400
1401

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

1402
1403
1404
1405
        second_half_lse_seqlen = None
        if causal and rank < (cp_size - 1):
            second_half_lse_seqlen = softmax_lse_per_step[-1].shape[-1]

1406
1407
        softmax_lse = softmax_lse.to(torch.float)
        for i in range(cp_size):
1408
            if i <= rank or not causal:
1409
                if qkv_format in ["bshd", "sbhd"]:
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
                    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,
                        )
1426
                elif qkv_format == "thd":
1427
1428
1429
1430
1431
                    tex.thd_out_correction(
                        out,
                        out_per_step[i],
                        softmax_lse,
                        softmax_lse_per_step[i],
1432
                        cu_seqlens_q_padded,
1433
                        False,
1434
                        softmax_lse_in_packed_format,
1435
                    )
1436
            else:
1437
                if qkv_format in ["bshd", "sbhd"]:
1438
1439
                    flash_attn_fwd_second_half_out_correction(
                        out,
1440
                        out_per_step[i],
1441
                        softmax_lse,
1442
                        softmax_lse_per_step[i],
1443
                        seq_dim,
1444
                    )
1445
                elif qkv_format == "thd":
1446
1447
1448
1449
1450
                    tex.thd_out_correction(
                        out,
                        out_per_step[i],
                        softmax_lse,
                        softmax_lse_per_step[i],
1451
                        cu_seqlens_q_padded,
1452
                        True,
1453
                        softmax_lse_in_packed_format,
1454
                    )
1455
1456

        kv = p2p_comm_buffers[-1]
1457
1458
1459
1460
1461
1462
1463
1464
        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:
1465
            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_after_attn(cp_size_a2a, out.device)
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
            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:
1477
            out = out.view(-1, *out.shape[-2:])
1478

1479
1480
        if fp8 and use_fused_attention:
            amax_cp_fwd = amax_per_step.amax(dim=1)
1481
1482
            S_quantizer.amax.copy_(amax_cp_fwd[0])
            O_CP_quantizer.amax.copy_(amax_cp_fwd[1])
1483

1484
        out_fp8 = None
1485
        out_f16 = out.to(qkv_dtype)
1486

1487
        if fp8 and (is_output_fp8 or int(os.getenv("NVTE_FP8_DPA_BWD", "1"))):
1488
1489
1490
            out_fp8 = O_quantizer(out_f16)  # final result

        out_ret = out_fp8 if (fp8 and is_output_fp8) else out_f16
1491
1492

        if fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
1493
            q_save, kv_save, out_save = q, kv, out_fp8._data
1494
        elif fp8 and is_input_fp8:
1495
            q_save, kv_save, out_save = q, kv, out_f16
1496
        else:
1497
            q_f16 = q_f16.view(q.shape)
1498
1499
            q_save, kv_save, out_save = q_f16, kv, out_f16

1500
        tensors_to_save, tensor_objects = prepare_for_saving(
1501
1502
1503
            q_save,
            kv_save,
            out_save,
1504
            softmax_lse,
1505
1506
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
1507
1508
            *cu_seqlens_q_per_step,
            *cu_seqlens_kv_per_step,
1509
1510
            *rng_states,
            *attn_biases,
1511
        )
1512
1513
1514
        ctx.save_for_backward(*tensors_to_save)
        ctx.tensor_objects = tensor_objects

1515
1516
1517
        ctx.cp_group_a2a = cp_group_a2a
        ctx.cp_size_a2a = cp_size_a2a
        ctx.rank_a2a = rank_a2a
1518
1519
        ctx.cp_group = cp_group
        ctx.cp_global_ranks = cp_global_ranks
1520
        ctx.cp_stream = cp_stream
1521
1522
        ctx.dropout_p = dropout_p
        ctx.max_seqlen_q = max_seqlen_q
1523
        ctx.max_seqlen_kv = max_seqlen_kv
1524
        ctx.softmax_scale = softmax_scale
1525
        ctx.qkv_format = qkv_format
1526
        ctx.attn_mask_type = attn_mask_type
1527
1528
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_bias_shape = None if attn_bias is None else attn_bias.shape
1529
        ctx.deterministic = deterministic
1530
        ctx.use_fused_attention = use_fused_attention
1531
        ctx.softmax_lse_in_packed_format = softmax_lse_in_packed_format
1532
        ctx.second_half_lse_seqlen = second_half_lse_seqlen
1533
1534
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        ctx.fp8_meta = fp8_meta
1535
1536
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
1537
        ctx.use_flash_attn_3 = use_flash_attn_3
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553

        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()
1554
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndKVP2P.forward")
1555

1556
        return out_ret
1557
1558
1559

    @staticmethod
    def backward(ctx, dout):
1560
        # pylint: disable=missing-function-docstring
1561
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndKVP2P.backward")
1562
1563
1564
        cp_size_a2a = ctx.cp_size_a2a
        rank_a2a = ctx.rank_a2a

1565
1566
        cp_size = get_distributed_world_size(ctx.cp_group)
        rank = get_distributed_rank(ctx.cp_group)
1567
1568
        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]
1569
        batch_p2p_comm = int(os.getenv("NVTE_BATCH_MHA_P2P_COMM", "0"))
1570

1571
        q, kv, out, softmax_lse, cu_seqlens_q_padded, cu_seqlens_kv_padded, *other_tensors = (
1572
            restore_from_saved(ctx.tensor_objects, ctx.saved_tensors)
1573
1574
1575
1576
1577
        )
        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]
1578

1579
1580
        causal = "causal" in ctx.attn_mask_type
        padding = "padding" in ctx.attn_mask_type
1581
1582

        seq_dim = None
1583
        if ctx.qkv_format in ["bshd", "sbhd"]:
1584
            seq_dim = ctx.qkv_format.index("s")
1585
1586
1587
            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
1588

1589
        if attn_biases[0] is not None:
1590
1591
            # [b, np, sq, 2*cp, sk//(2*cp)]
            attn_dbias = torch.zeros(
1592
                *ctx.attn_bias_shape, dtype=attn_biases[0].dtype, device=attn_biases[0].device
1593
1594
1595
            )
            # [b, np, sq, 2*cp, sk//(2*cp)] -> [b, np, 2, sq//2, 2*cp, sk//(2*cp)]
            attn_dbias_ = attn_dbias.view(
1596
                *attn_dbias.shape[:-3], 2, attn_dbias.shape[-3] // 2, *attn_dbias.shape[-2:]
1597
1598
1599
            )
        else:
            attn_dbias = None
1600
            attn_dbias_ = None
1601

1602
1603
        softmax_lse_ = None
        if causal and ctx.second_half_lse_seqlen is not None:
1604
            if ctx.qkv_format == "thd":
1605
                softmax_lse_ = tex.thd_read_second_half_lse(
1606
1607
1608
1609
                    softmax_lse,
                    cu_seqlens_q_padded,
                    ctx.softmax_lse_in_packed_format,
                    ctx.second_half_lse_seqlen,
1610
                )
1611
1612
            else:
                # [b, np, sq] -> [b, np, 2, sq//2]
1613
                softmax_lse_ = softmax_lse.view(*softmax_lse.shape[:-1], 2, -1)
1614
                softmax_lse_ = softmax_lse_[..., 1, :].contiguous()
1615
1616
1617
1618
1619
1620
            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)
1621
        if ctx.use_fused_attention:
1622
1623
1624
1625
            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]
1626
            softmax_lse.unsqueeze_(-1)
1627
            dout = dout.contiguous()
1628

1629
        dq = None
1630
        dout_dtype = dout.dtype
1631
1632
        fused_attn_backend = None
        fused_attn_dqkv_dtype = None
1633
1634
1635
        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)]
1636
1637
1638
        if ctx.fp8:
            if ctx.use_fused_attention:
                fused_attn_backend = FusedAttnBackend["FP8"]
1639

1640
                if ctx.is_output_fp8:
1641
                    assert isinstance(dout, Float8Tensor), "dout must be Float8Tensors for FP8 MHA!"
1642
                    ctx.dO_quantizer = dout._quantizer
1643
                else:
1644
                    dout = ctx.dO_quantizer(dout)
1645
1646
1647
1648
1649
1650
                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)
1651
                p2p_comm_buffers = [[kv, dkv_fp8], [torch.empty_like(kv), dkv_fp8_]]
1652
                dout = dout._data
1653
                fp8_meta_kwargs = {}
1654
                fp8_meta_kwargs["s_quantizer"] = ctx.S_quantizer
1655
1656
1657
                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()
1658
                    dP_quantizer_per_step[i].amax = amax_per_step[0][i].reshape((1,))
1659
                    dQKV_CP_quantizer_per_step[i] = ctx.dQKV_CP_quantizer.copy()
1660
                    dQKV_CP_quantizer_per_step[i].amax = amax_per_step[1][i].reshape((1,))
1661
1662
1663
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
            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
1681
1682
1683
1684
1685
1686
1687
1688
            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 = {}
1689
                fused_attn_dqkv_dtype = TE_DType[dout_dtype]
1690
1691
                fused_attn_backend = FusedAttnBackend["F16_arbitrary_seqlen"]

1692
1693
1694
1695
        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)
1696
1697
1698
            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_before_attn(
                cp_size_a2a, out.device
            )
1699
1700
1701
1702
1703
1704
1705
1706
1707
            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,
            )
1708
            if not ctx.fp8 and ctx.fp8_meta is not None and ctx.is_output_fp8:
1709
1710
1711
1712
                dout = ctx.dO_quantizer.create_tensor_from_data(
                    dout, fake_dtype=dout_dtype, internal=True
                )
                dout = dout.dequantize(dtype=dout_dtype)
1713

1714
1715
1716
1717
        out = out.view(*q.shape)
        dout = dout.view(*q.shape)
        send_recv_reqs = []

1718
        flash_attn_bwd = None
1719
1720
        if not ctx.use_fused_attention:
            fa_backward_kwargs = {"softmax_scale": ctx.softmax_scale}
1721
1722
1723
1724
            if ctx.use_flash_attn_3:
                flash_attn_bwd = (
                    _flash_attn_bwd_v3  # pylint: disable=possibly-used-before-assignment
                )
1725
1726
                fa_backward_kwargs["deterministic"] = ctx.deterministic
            else:
1727
1728
1729
1730
                if ctx.qkv_format == "thd":
                    flash_attn_bwd = _flash_attn_varlen_bwd
                else:
                    flash_attn_bwd = _flash_attn_bwd
1731
                fa_backward_kwargs["dropout_p"] = ctx.dropout_p
1732
                if fa_utils.v2_4_plus:
1733
                    fa_backward_kwargs["alibi_slopes"] = None
1734
                if fa_utils.v2_4_1_plus:
1735
                    fa_backward_kwargs["deterministic"] = ctx.deterministic
1736
                if fa_utils.v2_6_0_plus:
1737
                    fa_backward_kwargs["softcap"] = 0.0
1738

1739
1740
1741
1742
1743
        for i in range(cp_size):
            # wait until KV is received
            for req in send_recv_reqs:
                req.wait()

1744
1745
            send_tensor = p2p_comm_buffers[i % 2]
            recv_tensor = p2p_comm_buffers[(i + 1) % 2]
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
1773
1774
            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
                )
1775

1776
            kv = p2p_comm_buffers[i % 2][0]
1777
1778
            q_, kv_, out_, dout_ = None, None, None, None
            dq_, dk_, dv_ = None, None, None
1779
            # In reversed order of fwd
1780
            if causal:
1781
                if i == (cp_size - 1):
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
                    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
1796
                    if ctx.use_fused_attention:
1797
1798
1799
1800
1801
1802
1803
1804
                        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]]
1805
                        if attn_dbias is not None:
1806
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
                        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(
1827
                                dout_part, fake_dtype=dout_dtype, internal=True
1828
                            )
1829
1830
                            fp8_meta_kwargs["dp_quantizer"] = dP_quantizer_per_step[i]
                            fp8_meta_kwargs["dqkv_quantizer"] = dQKV_CP_quantizer_per_step[i]
1831
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
1832
                            ctx.max_seqlen_q,
1833
1834
1835
                            ctx.max_seqlen_kv,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
1836
1837
1838
1839
1840
                            q_part,
                            k_part,
                            v_part,
                            out_part,
                            dout_part,
1841
                            dout_dtype,
1842
                            fused_attn_dqkv_dtype,
1843
                            aux_ctx_tensors,
1844
                            fused_attn_backend,
1845
1846
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
                            cu_seqlens_kv_padded=cu_seqlens_kv_padded,
1847
1848
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
1849
                            qkv_layout=qkv_layout,
1850
                            attn_mask_type=ctx.attn_mask_type,
1851
                            attn_bias_type=ctx.attn_bias_type,
1852
1853
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
1854
                        )
1855
1856
1857
1858
                        if ctx.fp8:
                            dq_ = dq_._data
                            dk_ = dk_._data
                            dv_ = dv_._data
1859
                    else:
1860
                        dq_ = torch.empty_like(q_)
1861
                        dkv_ = torch.empty_like(kv_)
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
                        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
                        ):
1885
                            fa_backward_kwargs["window_size"] = (-1, 0)
1886
                        elif fa_utils.v2_7_0_plus:
1887
1888
                            fa_backward_kwargs["window_size_left"] = -1
                            fa_backward_kwargs["window_size_right"] = 0
1889
                        if not ctx.use_flash_attn_3:
1890
1891
                            fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                        flash_attn_bwd(
1892
1893
                            dout_,
                            q_,
1894
1895
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
1896
1897
                            out_,
                            softmax_lse,
1898
                            *fa_backward_args_thd,
1899
1900
                            causal=True,
                            **fa_backward_kwargs,
1901
                        )
1902
                elif i >= (cp_size - rank - 1):
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
                    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)
1919
                    if ctx.use_fused_attention:
1920
                        kv_ = kv_.contiguous()
1921
1922
1923
1924
1925
1926
1927
1928
                        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]]
1929
                        if attn_dbias is not None:
1930
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
                        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(
1951
                                dout_part, fake_dtype=dout_dtype, internal=True
1952
                            )
1953
1954
                            fp8_meta_kwargs["dp_quantizer"] = dP_quantizer_per_step[i]
                            fp8_meta_kwargs["dqkv_quantizer"] = dQKV_CP_quantizer_per_step[i]
1955
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
1956
                            ctx.max_seqlen_q,
1957
1958
1959
                            ctx.max_seqlen_kv // 2,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
1960
1961
1962
1963
1964
                            q_part,
                            k_part,
                            v_part,
                            out_part,
                            dout_part,
1965
                            dout_dtype,
1966
                            fused_attn_dqkv_dtype,
1967
                            aux_ctx_tensors,
1968
                            fused_attn_backend,
1969
1970
1971
1972
                            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
                            ),
1973
1974
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
1975
                            qkv_layout=qkv_layout,
1976
                            attn_mask_type="padding" if padding else "no_mask",
1977
                            attn_bias_type=ctx.attn_bias_type,
1978
1979
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
1980
                        )
1981
1982
1983
1984
                        if ctx.fp8:
                            dq_ = dq_._data
                            dk_ = dk_._data
                            dv_ = dv_._data
1985
                    else:
1986
                        dq_ = torch.empty_like(q_)
1987
                        dkv_ = torch.empty_like(kv_)
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
                        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
                        ):
2011
                            fa_backward_kwargs["window_size"] = (-1, -1)
2012
                        elif fa_utils.v2_7_0_plus:
2013
2014
                            fa_backward_kwargs["window_size_left"] = -1
                            fa_backward_kwargs["window_size_right"] = -1
2015
                        if not ctx.use_flash_attn_3:
2016
2017
                            fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                        flash_attn_bwd(
2018
2019
                            dout_,
                            q_,
2020
2021
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
2022
2023
                            out_,
                            softmax_lse,
2024
                            *fa_backward_args_thd,
2025
2026
                            causal=False,
                            **fa_backward_kwargs,
2027
2028
                        )
                else:
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
                    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
2046
                    if ctx.use_fused_attention:
2047
                        q_, out_, dout_ = [x.contiguous() for x in [q_, out_, dout_]]
2048
2049
2050
2051
2052
2053
2054
2055
                        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]]
2056
                        if attn_dbias is not None:
2057
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078

                        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(
2079
                                dout_part, fake_dtype=dout_dtype, internal=True
2080
                            )
2081
2082
                            fp8_meta_kwargs["dp_quantizer"] = dP_quantizer_per_step[i]
                            fp8_meta_kwargs["dqkv_quantizer"] = dQKV_CP_quantizer_per_step[i]
2083
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2084
                            ctx.max_seqlen_q // 2,
2085
2086
2087
                            ctx.max_seqlen_kv,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2088
2089
2090
2091
2092
                            q_part,
                            k_part,
                            v_part,
                            out_part,
                            dout_part,
2093
                            dout_dtype,
2094
                            fused_attn_dqkv_dtype,
2095
                            aux_ctx_tensors,
2096
                            fused_attn_backend,
2097
2098
2099
2100
                            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,
2101
2102
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
2103
                            qkv_layout=qkv_layout,
2104
                            attn_mask_type="padding" if padding else "no_mask",
2105
                            attn_bias_type=ctx.attn_bias_type,
2106
2107
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
2108
                        )
2109
2110
2111
2112
                        if ctx.fp8:
                            dq_ = dq_._data
                            dk_ = dk_._data
                            dv_ = dv_._data
2113
                    else:
2114
                        dq_ = torch.empty_like(q_)
2115
                        dkv_ = torch.empty_like(kv_)
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137
2138
                        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
                        ):
2139
                            fa_backward_kwargs["window_size"] = (-1, -1)
2140
                        elif fa_utils.v2_7_0_plus:
2141
2142
                            fa_backward_kwargs["window_size_left"] = -1
                            fa_backward_kwargs["window_size_right"] = -1
2143
                        if not ctx.use_flash_attn_3:
2144
2145
                            fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                        flash_attn_bwd(
2146
2147
                            dout_,
                            q_,
2148
2149
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
2150
2151
                            out_,
                            softmax_lse_,
2152
                            *fa_backward_args_thd,
2153
2154
                            causal=False,
                            **fa_backward_kwargs,
2155
2156
2157
                        )
            else:
                if ctx.use_fused_attention:
2158
2159
2160
2161
                    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]]
2162
                    if attn_dbias is not None:
2163
                        aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2164
2165
2166
2167
2168
2169
2170
2171
                    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(
2172
                            q_part, fake_dtype=ctx.qkv_dtype, internal=True
2173
2174
                        )
                        k_part = ctx.QKV_quantizer.create_tensor_from_data(
2175
                            k_part, fake_dtype=ctx.qkv_dtype, internal=True
2176
2177
                        )
                        v_part = ctx.QKV_quantizer.create_tensor_from_data(
2178
                            v_part, fake_dtype=ctx.qkv_dtype, internal=True
2179
2180
                        )
                        out_part = ctx.O_quantizer.create_tensor_from_data(
2181
                            out_part, fake_dtype=ctx.qkv_dtype, internal=True
2182
2183
                        )
                        dout_part = ctx.dO_quantizer.create_tensor_from_data(
2184
                            dout_part, fake_dtype=dout_dtype, internal=True
2185
                        )
2186
2187
                        fp8_meta_kwargs["dp_quantizer"] = dP_quantizer_per_step[i]
                        fp8_meta_kwargs["dqkv_quantizer"] = dQKV_CP_quantizer_per_step[i]
2188
                    dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2189
                        ctx.max_seqlen_q,
2190
2191
2192
                        ctx.max_seqlen_kv,
                        cu_seqlens_q_per_step[cp_size - i - 1],
                        cu_seqlens_kv_per_step[cp_size - i - 1],
2193
2194
2195
2196
2197
                        q_part,
                        k_part,
                        v_part,
                        out_part,
                        dout_part,
2198
                        dout_dtype,
2199
                        fused_attn_dqkv_dtype,
2200
                        aux_ctx_tensors,
2201
                        fused_attn_backend,
2202
2203
                        cu_seqlens_q_padded=cu_seqlens_q_padded,
                        cu_seqlens_kv_padded=cu_seqlens_kv_padded,
2204
2205
                        attn_scale=ctx.softmax_scale,
                        dropout=ctx.dropout_p,
2206
                        qkv_layout=qkv_layout,
2207
                        attn_mask_type=ctx.attn_mask_type,
2208
                        attn_bias_type=ctx.attn_bias_type,
2209
2210
                        deterministic=ctx.deterministic,
                        **fp8_meta_kwargs,
2211
                    )
2212
2213
2214
2215
2216
2217

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

2218
                else:
2219
2220
                    dq_ = torch.empty_like(q)
                    dkv_ = torch.empty_like(kv)
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
                    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):
2234
                        fa_backward_kwargs["window_size"] = (-1, -1)
2235
                    elif fa_utils.v2_7_0_plus:
2236
2237
                        fa_backward_kwargs["window_size_left"] = -1
                        fa_backward_kwargs["window_size_right"] = -1
2238
                    if not ctx.use_flash_attn_3:
2239
2240
                        fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                    flash_attn_bwd(
2241
2242
2243
2244
2245
                        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,
2246
                        softmax_lse,
2247
                        *fa_backward_args_thd,
2248
2249
                        causal=False,
                        **fa_backward_kwargs,
2250
2251
                    )

2252
2253
            if ctx.fp8:
                dq = dq_fp8[(rank + i + 1) % cp_size]
2254
2255
2256
            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]
2257
                dq_ = dq_.view(*dq.shape)
2258

2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
            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:
2270
                if i > (cp_size - rank - 1):
2271
                    dq.add_(dq_)
2272
2273
                elif i == (cp_size - rank - 1):
                    if rank == (cp_size - 1):
2274
2275
                        dq.copy_(dq_)
                    else:
2276
2277
2278
2279
2280
2281
                        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])
2282
                        elif ctx.qkv_format == "thd":
2283
                            tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "copy", "add")
2284
                elif i > 0:
2285
2286
2287
2288
                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].add_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].add_(dq_)
2289
                    elif ctx.qkv_format == "thd":
2290
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "none", "add")
2291
                else:
2292
2293
2294
2295
                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].copy_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].copy_(dq_)
2296
                    elif ctx.qkv_format == "thd":
2297
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "none", "copy")
2298
2299
2300
2301
2302
            else:
                if i == 0:
                    dq.copy_(dq_)
                else:
                    dq.add_(dq_)
2303

2304
            if attn_dbias is not None:
2305
                idx = (rank + i + 1) % cp_size
2306
                if i == (cp_size - 1) or not causal:
2307
                    # [b, np, sq, sk//cp] -> [b, np, sq, 2, sk//(2*cp)]
2308
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1] // 2)
2309
                    attn_dbias[..., idx, :].copy_(dbias_[..., 0, :])
2310
2311
                    attn_dbias[..., (2 * cp_size - idx - 1), :].copy_(dbias_[..., 1, :])
                elif i >= (cp_size - rank - 1):
2312
2313
2314
2315
                    # [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)]
2316
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1] // 2)
2317
                    attn_dbias_[..., 1, :, idx, :].copy_(dbias_[..., 0, :])
2318
                    attn_dbias_[..., 1, :, (2 * cp_size - idx - 1), :].copy_(dbias_[..., 1, :])
2319

2320
2321
2322
            # wait until dKV is received
            for req in send_recv_reqs:
                req.wait()
2323

2324
2325
2326
2327
2328
2329
2330
            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]
2331
            if ctx.use_fused_attention:
2332
                if ctx.qkv_format in ["bshd", "sbhd"]:
2333
2334
2335
2336
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
                    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)
2347

2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
            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:
2359
                if i == (cp_size - 1):
2360
                    if rank == 0:
2361
2362
2363
2364
2365
2366
                        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, ...])
2367
                        elif ctx.qkv_format == "thd":
2368
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "add", "copy")
2369
2370
                    else:
                        dkv.add_(dkv_)
2371
2372
                elif i >= (cp_size - rank - 1):
                    if i == 0 and rank == (cp_size - 1):
2373
2374
2375
2376
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].copy_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].copy_(dkv_)
2377
                        elif ctx.qkv_format == "thd":
2378
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "copy", "none")
2379
                    else:
2380
2381
2382
2383
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].add_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].add_(dkv_)
2384
                        elif ctx.qkv_format == "thd":
2385
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "add", "none")
2386
2387
2388
2389
2390
                elif i > 0:
                    dkv.add_(dkv_)
                else:
                    dkv.copy_(dkv_)
            else:
2391
2392
2393
2394
2395
                if i == 0:
                    dkv.copy_(dkv_)
                else:
                    dkv.add_(dkv_)

2396
        if ctx.fp8 and ctx.use_fused_attention:
2397
            amax_cp_bwd = amax_per_step.amax(dim=1)
2398
2399
            ctx.dP_quantizer.amax.copy_(amax_cp_bwd[0])
            ctx.dQKV_CP_quantizer.amax.copy_(amax_cp_bwd[1])
2400
2401
2402
2403
            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:])
2404
2405
2406
2407
2408
2409
2410
            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]]
2411
2412
            dq, dkv = [x.sum(dim=0).to(dout_dtype) for x in [dq, dkv]]

2413
        if causal:
2414
2415
            if ctx.qkv_format == "bshd":
                # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
2416
                dq = dq.view(dq.shape[0], -1, *dq.shape[-2:])
2417
                # [2, b, 2, sk//2, np, hn] -> [2, b, sk, np, hn]
2418
                dkv = dkv.view(*dkv.shape[0:2], -1, *dkv.shape[-2:])
2419
2420
            elif ctx.qkv_format == "sbhd":
                # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
2421
                dq = dq.view(-1, *dq.shape[-3:])
2422
                # [2, 2, sk//2, b, np, hn] -> [2, sk, b, np, hn]
2423
2424
                dkv = dkv.view(dkv.shape[0], -1, *dkv.shape[-3:])

2425
2426
2427
        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)
2428

2429
        if ctx.fp8 and ctx.is_input_fp8:
2430
2431
            assert torch.uint8 not in [dq.dtype, dkv.dtype]
            dq, dkv = [ctx.dQKV_quantizer(x)._data for x in [dq, dkv]]
2432
2433
2434
        dk, dv = dkv[0], dkv[1]

        if cp_size_a2a > 1:
2435
            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_after_attn(cp_size_a2a, q.device)
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
            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]]

2450
2451
2452
        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)
2453
2454
        # converting torch.uint8 to float8tensor
        if ctx.fp8 and ctx.is_input_fp8:
2455
2456
2457
            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)
2458
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndKVP2P.backward")
2459

2460
2461
2462
        return (
            None,
            dq,
2463
2464
            dk,
            dv,
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
2476
            attn_dbias,
2477
2478
2479
2480
2481
            None,
            None,
            None,
            None,
            None,
2482
2483
            None,
            None,
2484
            None,
2485
            None,
2486
            None,
2487
        )
2488
2489


2490
2491
def get_kv_seq_info_after_all_gather(
    local_chunk_id, cp_size, max_seqlen_q, max_seqlen_kv, window_size, causal
2492
):
2493
2494
2495
2496
2497
2498
2499
2500
2501
2502
2503
2504
2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
    """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)
2515
2516
2517
2518


class AttnFuncWithCPAndKVAllGather(torch.autograd.Function):
    """
2519
2520
    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>`_.
2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
2535
2536
2537
2538
2539
2540
2541
2542
    """

    @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,
2543
2544
        cp_group,
        cp_stream,
2545
        use_flash_attn_3,
2546
    ):
2547
        # pylint: disable=missing-function-docstring
2548
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndKVAllGather.forward")
2549
2550
2551
2552
2553
2554
        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)

2555
2556
        qkv_dtype = q.dtype

2557
2558
        causal = "causal" in attn_mask_type
        padding = "padding" in attn_mask_type
2559
        assert not padding, f"{attn_mask_type} mask type is not supported!"
2560
2561
2562
2563
2564
        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 (
2565
            use_fused_attention or fa_utils.v2_3_plus
2566
        ), "Sliding window attention only can work with FusedAttention or FlashAttention >= 2.3!"
2567

2568
        flash_attn_fwd = None
2569
2570
        if not use_fused_attention:
            fa_forward_kwargs = {"softmax_scale": softmax_scale}
2571
2572
            if use_flash_attn_3:
                flash_attn_fwd = _flash_attn_fwd_v3
2573
            else:
2574
2575
2576
2577
                if qkv_format == "thd":
                    flash_attn_fwd = _flash_attn_varlen_fwd
                else:
                    flash_attn_fwd = _flash_attn_fwd
2578
2579
                fa_forward_kwargs["dropout_p"] = dropout_p
                fa_forward_kwargs["return_softmax"] = False
2580
                if fa_utils.v2_4_plus:
2581
                    fa_forward_kwargs["alibi_slopes"] = None
2582
                if fa_utils.v2_5_7_plus and qkv_format == "thd":
2583
                    fa_forward_kwargs["block_table"] = None
2584
                if fa_utils.v2_6_0_plus:
2585
                    fa_forward_kwargs["softcap"] = 0.0
2586
2587
2588
2589
2590
2591
2592
2593
2594
2595
2596

        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)
2597
2598
        if use_fused_attention or qkv_format == "thd":
            cu_seqlens_q = cu_seqlens_q // (2 * cp_size)
2599
2600
2601
2602
        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
2603

2604
2605
2606
2607
        # [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]]
2608

2609
        # [s, b, np, hn] -> [cp, s, b, np, hn]
2610
2611
        k_ag, _ = gather_along_first_dim(k, cp_group)
        v_ag, _ = gather_along_first_dim(v, cp_group)
2612
2613

        # [cp, s, b, np, hn] -> [cp*2, s//2, b, np, hn]
2614
2615
        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:])
2616
        chunk_ids_for_kv_ag = get_seq_chunk_ids_for_reordering_before_attn(cp_size, k.device)
2617
2618
2619
2620
2621
2622
2623
2624
2625
        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]
2626
2627

        local_seq_chunk_ids = [rank, 2 * cp_size - rank - 1]
2628
2629
2630
        kv_seq_range_per_step = [None, None]
        window_size_per_step = [None, None]
        cu_seqlens_kv_per_step = [None, None]
2631
2632
2633
2634
2635
2636
2637
2638
        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]):
2639
2640
                    # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                    # or [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
2641
2642
2643
2644
2645
2646
2647
2648
2649
                    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,
2650
                        )
2651
2652
2653
2654
2655
2656
                    )
                    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
2657
                    if use_fused_attention or qkv_format == "thd":
2658
                        cu_seqlens_kv_per_step[i] = dpa_utils.get_full_cu_seqlens(
2659
2660
                            k.shape[1], max_seqlen_kv_, k.device
                        )
2661
2662
2663
                    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_]]
2664
2665
2666
2667
                    if use_fused_attention:
                        out_per_step[i], [softmax_lse_per_step[i], rng_states[i]] = fused_attn_fwd(
                            is_training,
                            max_seqlen_q,
2668
                            max_seqlen_kv_,
2669
                            cu_seqlens_q,
2670
                            cu_seqlens_kv_per_step[i],
2671
2672
2673
                            q_,
                            k_,
                            v_,
2674
                            qkv_dtype,
2675
2676
2677
2678
2679
2680
2681
2682
                            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,
2683
2684
                            cu_seqlens_kv_padded=cu_seqlens_kv_per_step[i],
                            window_size=window_size_per_step[i],
2685
2686
                        )
                    else:
2687
2688
2689
2690
2691
2692
2693
2694
2695
2696
                        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):
2697
                            fa_forward_kwargs["window_size"] = window_size_per_step[i]
2698
                        elif fa_utils.v2_7_0_plus:
2699
2700
                            fa_forward_kwargs["window_size_left"] = window_size_per_step[i][0]
                            fa_forward_kwargs["window_size_right"] = window_size_per_step[i][1]
2701
2702
2703
2704
                        fa_outputs = flash_attn_fwd(
                            q_,
                            k_,
                            v_,
2705
                            *fa_forward_args_thd,
2706
2707
                            causal=causal,
                            **fa_forward_kwargs,
2708
                        )
2709
                        if not fa_utils.v2_7_0_plus:
2710
2711
                            out_per_step[i] = fa_outputs[4]
                            softmax_lse_per_step[i] = fa_outputs[5]
2712
                            if not use_flash_attn_3:
2713
2714
2715
2716
                                rng_states[i] = fa_outputs[7]
                        else:
                            out_per_step[i] = fa_outputs[0]
                            softmax_lse_per_step[i] = fa_outputs[1]
2717
                            if not use_flash_attn_3:
2718
                                rng_states[i] = fa_outputs[3]
2719
2720
2721
2722

            if i > 0:
                with torch.cuda.stream(flash_attn_streams[i - 1]):
                    if qkv_format == "bshd":
2723
                        out[:, i - 1].copy_(out_per_step[i - 1])
2724
                    elif qkv_format == "sbhd":
2725
                        out[i - 1].copy_(out_per_step[i - 1])
2726
2727
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742

        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,
2743
            *cu_seqlens_kv_per_step,
2744
2745
2746
2747
            *out_per_step,
            *softmax_lse_per_step,
            *rng_states,
        )
2748
2749

        ctx.qkv_dtype = qkv_dtype
2750
2751
        ctx.kv_seq_range_per_step = kv_seq_range_per_step
        ctx.window_size_per_step = window_size_per_step
2752
2753
2754
2755
2756
2757
2758
        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
2759
        ctx.attn_mask_type = attn_mask_type
2760
2761
        ctx.deterministic = deterministic
        ctx.use_fused_attention = use_fused_attention
2762
        ctx.use_flash_attn_3 = use_flash_attn_3
2763
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndKVAllGather.forward")
2764
2765
2766
2767
        return out

    @staticmethod
    def backward(ctx, dout):
2768
        # pylint: disable=missing-function-docstring
2769
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndKVAllGather.backward")
2770
2771
2772
        cp_size = get_distributed_world_size(ctx.cp_group)
        rank = get_distributed_rank(ctx.cp_group)

2773
2774
2775
2776
2777
2778
        (*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]
2779
2780
        kv_seq_range_per_step = ctx.kv_seq_range_per_step
        window_size_per_step = ctx.window_size_per_step
2781

2782
        seq_dim = ctx.qkv_format.index("s")
2783
2784
        qkv_layout = ctx.qkv_format + "_" + ctx.qkv_format + "_" + ctx.qkv_format

2785
        dout = dout.view(q.shape)
2786
        dq = torch.empty_like(q)
2787
        dk = torch.zeros((k.shape[0] * cp_size, *k.shape[1:]), dtype=k.dtype, device=k.device)
2788
2789
2790
2791
2792
2793
2794
2795
2796
2797
        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()

2798
        # [s, b, np, hn] -> [cp, s, b, np, hn]
2799
2800
        k_ag, _ = gather_along_first_dim(k, ctx.cp_group)
        v_ag, _ = gather_along_first_dim(v, ctx.cp_group)
2801
2802

        # [cp, s, b, np, hn] -> [cp*2, s//2, b, np, hn]
2803
2804
        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:])
2805
        chunk_ids_for_kv_ag = get_seq_chunk_ids_for_reordering_before_attn(cp_size, k.device)
2806
2807
2808
2809
2810
2811
        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())
2812
2813
2814

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

2815
        flash_attn_bwd = None
2816
2817
        if not ctx.use_fused_attention:
            fa_backward_kwargs = {"softmax_scale": ctx.softmax_scale}
2818
2819
            if ctx.use_flash_attn_3:
                flash_attn_bwd = _flash_attn_bwd_v3
2820
2821
                fa_backward_kwargs["deterministic"] = ctx.deterministic
            else:
2822
2823
2824
2825
                if ctx.qkv_format == "thd":
                    flash_attn_bwd = _flash_attn_varlen_bwd
                else:
                    flash_attn_bwd = _flash_attn_bwd
2826
                fa_backward_kwargs["dropout_p"] = ctx.dropout_p
2827
                if fa_utils.v2_4_plus:
2828
                    fa_backward_kwargs["alibi_slopes"] = None
2829
                if fa_utils.v2_4_1_plus:
2830
                    fa_backward_kwargs["deterministic"] = ctx.deterministic
2831
                if fa_utils.v2_6_0_plus:
2832
                    fa_backward_kwargs["softcap"] = 0.0
2833
2834
2835
2836

        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]):
2837
2838
                    # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                    # or [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
2839
2840
2841
2842
2843
2844
2845
2846
2847
                    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_]]
2848
                    out_ = out_per_step[i]
2849
                    dout_ = dout.select(seq_dim, i).contiguous().view(out_.shape)
2850
2851
2852
2853
                    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,
2854
                            max_seqlen_kv,
2855
                            cu_seqlens_q,
2856
                            cu_seqlens_kv_per_step[i],
2857
2858
2859
2860
2861
                            q_,
                            k_,
                            v_,
                            out_,
                            dout_,
2862
                            ctx.qkv_dtype,
2863
                            TE_DType[dout.dtype],
2864
2865
2866
                            aux_ctx_tensors,
                            tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
2867
                            cu_seqlens_kv_padded=cu_seqlens_kv_per_step[i],
2868
2869
2870
2871
2872
                            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,
2873
2874
                            window_size=window_size_per_step[i],
                            deterministic=ctx.deterministic,
2875
2876
2877
2878
2879
                        )
                    else:
                        dq_per_step[i], dk_per_step[i], dv_per_step[i] = [
                            torch.empty_like(x) for x in [q_, k_, v_]
                        ]
2880
2881
2882
2883
2884
2885
2886
2887
2888
2889
2890
2891
2892
                        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:
2893
                            fa_backward_kwargs["rng_state"] = rng_states[i]
2894
2895
2896
                        if ctx.use_flash_attn_3 or (
                            fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus
                        ):
2897
                            fa_backward_kwargs["window_size"] = window_size_per_step[i]
2898
                        elif fa_utils.v2_7_0_plus:
2899
2900
                            fa_backward_kwargs["window_size_left"] = window_size_per_step[i][0]
                            fa_backward_kwargs["window_size_right"] = window_size_per_step[i][1]
2901
                        flash_attn_bwd(
2902
2903
2904
2905
2906
2907
                            dout_,
                            q_,
                            k_,
                            v_,
                            out_,
                            softmax_lse_per_step[i],
2908
                            *fa_backward_args_thd,
2909
2910
                            causal="causal" in ctx.attn_mask_type,
                            **fa_backward_kwargs,
2911
2912
2913
2914
2915
                        )

            if i > 0:
                with torch.cuda.stream(flash_attn_streams[i - 1]):
                    if ctx.qkv_format == "bshd":
2916
                        dq[:, i - 1].copy_(dq_per_step[i - 1])
2917
                    elif ctx.qkv_format == "sbhd":
2918
2919
2920
2921
2922
2923
                        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]]
                    ]
2924
2925
2926
                    # wait until dkv update of last step is done
                    if i > 1:
                        flash_attn_streams[i - 1].wait_event(dkv_update_done)
2927
2928
2929
2930
2931
2932
                    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])
2933
2934
2935
2936
2937
                    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)

2938
2939
2940
        # [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:])
2941
        chunk_ids_for_kv_ag = get_seq_chunk_ids_for_reordering_after_attn(cp_size, dk.device)
2942
2943
2944
        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]
2945
2946
2947
2948
2949
        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)

2950
2951
2952
        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()
2953
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndKVAllGather.backward")
2954
2955
2956
2957
2958
2959
2960
2961
2962
2963
2964
2965
2966
2967
2968
2969
2970
2971
2972
2973
2974

        return (
            None,
            dq,
            dk,
            dv,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
2975
            None,
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
3009
3010
        )


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,
3011
        quantizers,
3012
        use_flash_attn_3,
3013
    ):
3014
        # pylint: disable=missing-function-docstring
3015
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndQKVOA2A.forward")
3016
3017
3018
3019
        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)

        cp_size = get_distributed_world_size(cp_group)
3020
        qkv_dtype = q.dtype
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030

        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
3031
            or fa_utils.v2_3_plus
3032
        ), "Sliding window attention only can work with FusedAttention or FlashAttention >= 2.3!"
3033

3034
        flash_attn_fwd = None
3035
3036
        if not use_fused_attention:
            fa_forward_kwargs = {"softmax_scale": softmax_scale}
3037
3038
            if use_flash_attn_3:
                flash_attn_fwd = _flash_attn_fwd_v3
3039
3040
                fa_forward_kwargs["window_size"] = window_size
            else:
3041
3042
3043
3044
                if qkv_format == "thd":
                    flash_attn_fwd = _flash_attn_varlen_fwd
                else:
                    flash_attn_fwd = _flash_attn_fwd
3045
3046
                fa_forward_kwargs["dropout_p"] = dropout_p
                fa_forward_kwargs["return_softmax"] = False
3047
                if fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus:
3048
                    fa_forward_kwargs["window_size"] = window_size
3049
                elif fa_utils.v2_7_0_plus:
3050
3051
                    fa_forward_kwargs["window_size_left"] = window_size[0]
                    fa_forward_kwargs["window_size_right"] = window_size[1]
3052
                if fa_utils.v2_4_plus:
3053
                    fa_forward_kwargs["alibi_slopes"] = None
3054
                if fa_utils.v2_5_7_plus and qkv_format == "thd":
3055
                    fa_forward_kwargs["block_table"] = None
3056
                if fa_utils.v2_6_0_plus:
3057
                    fa_forward_kwargs["softcap"] = 0.0
3058
3059
3060
3061
3062
3063
3064
3065
3066
3067
3068
3069
3070
3071

        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!"

3072
        fused_attn_backend = None
3073
3074
        # "fp8_mha" decides outputs in fp8, while inputs are inferred from the real dtype
        is_input_fp8 = False
3075
3076
3077
        is_output_fp8 = False

        QKV_quantizer, O_quantizer, S_quantizer, dQKV_quantizer, dO_quantizer, dP_quantizer = (
3078
            dpa_utils.get_attention_quantizers(fp8, quantizers, cp_specific_quantizers=False)
3079
3080
3081
        )
        if fp8:
            if use_fused_attention:
3082
                fused_attn_backend = FusedAttnBackend["FP8"]
3083
3084
3085
3086
                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)
3087
                is_output_fp8 = fp8_meta is not None and fp8_meta["recipe"].fp8_mha
3088
                if is_input_fp8:
3089
                    QKV_quantizer = q._quantizer
3090
3091
3092
3093
                    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
3094
                    q, k, v = [QKV_quantizer(x)._data for x in [q_f16, k_f16, v_f16]]
3095
                fp8_meta_kwargs = {}
3096
3097
                fp8_meta_kwargs["s_quantizer"] = S_quantizer
                fp8_meta_kwargs["o_quantizer"] = O_quantizer  # partial result quantizer
3098
3099
3100
3101
3102
3103
3104
            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"]

3105
        chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_before_attn(cp_size, q.device)
3106
3107
3108
3109
        q, k, v = flash_attn_a2a_communicate(
            [q, k, v], chunk_ids_for_a2a, seq_dim, cp_size, cp_group, cp_stream, True
        )

3110
        if fp8 and not is_input_fp8 and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
3111
            q_f16, k_f16, v_f16 = q, k, v
3112
            q, k, v = [QKV_quantizer(x)._data for x in [q_f16, k_f16, v_f16]]
3113
3114
3115

        batch_size = q.shape[batch_dim]
        if use_fused_attention:
3116
3117
3118
3119
3120
3121
3122
3123
3124
3125
3126
            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
                )
3127
3128
3129
3130
3131
3132
            out, aux_ctx_tensors = fused_attn_fwd(
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
3133
3134
3135
3136
                q_part,
                k_part,
                v_part,
                qkv_dtype,
3137
3138
3139
3140
3141
3142
3143
3144
3145
3146
3147
3148
                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,
            )
3149
3150
            if fp8:
                out = out._data
3151
        else:
3152
3153
3154
3155
3156
3157
3158
3159
3160
            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,
            )
3161
            fa_outputs = flash_attn_fwd(
3162
3163
3164
                q,
                k,
                v,
3165
                *fa_forward_args_thd,
3166
                causal=causal,
3167
                **fa_forward_kwargs,
3168
            )
3169
            if not fa_utils.v2_7_0_plus:
3170
                out, softmax_lse = fa_outputs[4], fa_outputs[5]
3171
                rng_state = fa_outputs[7] if not use_flash_attn_3 else None
3172
3173
            else:
                out, softmax_lse = fa_outputs[0], fa_outputs[1]
3174
                rng_state = fa_outputs[3] if not use_flash_attn_3 else None
3175
3176
            aux_ctx_tensors = [softmax_lse, rng_state]

3177
        chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_after_attn(cp_size, out.device)
3178
3179
3180
3181
3182
3183
3184
3185
3186
3187
3188
3189
3190
        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:
3191
            if is_output_fp8:
3192
3193
                out_fp8 = O_quantizer.create_tensor_from_data(
                    out, fake_dtype=qkv_dtype, internal=False
3194
3195
                )
                out_ret = out_fp8
3196
                out = out_fp8._data
3197
            else:
3198
                out_fp8 = O_quantizer.create_tensor_from_data(
3199
                    out, fake_dtype=qkv_dtype, internal=True
3200
                )
3201
                out_f16 = out_fp8.dequantize(dtype=qkv_dtype)
3202
3203
3204
3205
                out_ret = out_f16
        else:
            out_ret = out

3206
        if not fp8 or int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
3207
            q_save, k_save, v_save, out_save = q, k, v, out
3208
3209
3210
3211
3212
3213
3214
3215
3216
        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
3217

3218
        tensors_to_save, tensor_objects = prepare_for_saving(
3219
3220
3221
3222
3223
3224
3225
3226
3227
3228
            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,
        )
3229
3230
3231
        ctx.save_for_backward(*tensors_to_save)
        ctx.tensor_objects = tensor_objects

3232
3233
3234
3235
3236
3237
3238
3239
3240
3241
3242
3243
3244
3245
3246
        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
3247
3248
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
3249
        ctx.use_flash_attn_3 = use_flash_attn_3
3250
3251
3252
3253
3254
3255
3256
3257
3258
3259
3260
3261
3262
3263
3264

        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()
3265
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndQKVOA2A.forward")
3266
3267
3268
3269
        return out_ret

    @staticmethod
    def backward(ctx, dout):
3270
        # pylint: disable=missing-function-docstring
3271
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndQKVOA2A.backward")
3272
3273
        cp_size = get_distributed_world_size(ctx.cp_group)

3274
3275
3276
3277
3278
3279
3280
3281
3282
3283
3284
        (
            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)
3285
3286
3287
3288
3289

        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")

3290
        dout_dtype = dout.dtype
3291
3292
        fused_attn_backend = None
        fused_attn_dqkv_dtype = None
3293
3294
3295
        if ctx.fp8:
            if ctx.use_fused_attention:
                fused_attn_backend = FusedAttnBackend["FP8"]
3296
                if ctx.is_output_fp8:
3297
                    assert isinstance(dout, Float8Tensor), "dout must be Float8Tensors for FP8 MHA!"
3298
                    ctx.dO_quantizer = dout._quantizer
3299
                else:
3300
                    dout = ctx.dO_quantizer(dout)
3301
                fused_attn_dqkv_dtype = TE_DType[dout._data.dtype]
3302
                dout = dout._data
3303
                fp8_meta_kwargs = {}
3304
3305
3306
3307
                fp8_meta_kwargs["s_quantizer"] = ctx.S_quantizer
                fp8_meta_kwargs["dp_quantizer"] = ctx.dP_quantizer
                fp8_meta_kwargs["dqkv_quantizer"] = ctx.dQKV_quantizer

3308
3309
3310
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
3311
3312
3313
3314
3315
3316
3317
3318
3319
3320
3321
3322
3323
3324
3325
3326
            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]]
3327
3328
            if ctx.use_fused_attention:
                fp8_meta_kwargs = {}
3329
                fused_attn_dqkv_dtype = TE_DType[dout_dtype]
3330
3331
3332
3333
3334
3335
                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)

3336
        chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_before_attn(cp_size, out.device)
3337
3338
3339
        out, dout = flash_attn_a2a_communicate(
            [out, dout], chunk_ids_for_a2a, seq_dim, cp_size, ctx.cp_group, ctx.cp_stream, True
        )
3340
3341
3342
3343
3344
3345
3346
3347
3348
        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)
3349

3350
        flash_attn_bwd = None
3351
3352
        if not ctx.use_fused_attention:
            fa_backward_kwargs = {"softmax_scale": ctx.softmax_scale}
3353
3354
3355
3356
            if ctx.use_flash_attn_3:
                flash_attn_bwd = (
                    _flash_attn_bwd_v3  # pylint: disable=possibly-used-before-assignment
                )
3357
3358
3359
                fa_backward_kwargs["window_size"] = ctx.window_size
                fa_backward_kwargs["deterministic"] = ctx.deterministic
            else:
3360
3361
3362
3363
                if ctx.qkv_format == "thd":
                    flash_attn_bwd = _flash_attn_varlen_bwd
                else:
                    flash_attn_bwd = _flash_attn_bwd
3364
                fa_backward_kwargs["dropout_p"] = ctx.dropout_p
3365
                if fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus:
3366
                    fa_backward_kwargs["window_size"] = ctx.window_size
3367
                elif fa_utils.v2_7_0_plus:
3368
3369
                    fa_backward_kwargs["window_size_left"] = ctx.window_size[0]
                    fa_backward_kwargs["window_size_right"] = ctx.window_size[1]
3370
                if fa_utils.v2_4_plus:
3371
                    fa_backward_kwargs["alibi_slopes"] = None
3372
                if fa_utils.v2_4_1_plus:
3373
                    fa_backward_kwargs["deterministic"] = ctx.deterministic
3374
                if fa_utils.v2_6_0_plus:
3375
                    fa_backward_kwargs["softcap"] = 0.0
3376
3377

        if ctx.use_fused_attention:
3378
3379
3380
3381
3382
3383
3384
3385
3386
3387
3388
3389
3390
3391
3392
3393
3394
3395
3396
3397
            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(
3398
                    dout_part, fake_dtype=dout_dtype, internal=True
3399
3400
                )

3401
3402
3403
3404
3405
            dq, dk, dv, _ = fused_attn_bwd(
                ctx.max_seqlen_q,
                ctx.max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
3406
3407
3408
3409
3410
                q_part,
                k_part,
                v_part,
                out_part,
                dout_part,
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                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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3905
            device=torch.cuda.current_device(),
        )

3906
        scale = self.softmax_scale
3907
        if apply_qk_layer_scaling:
3908
            scale /= self.layer_number
3909
3910

        # Raw attention scores. [b * np, sq, sk]
3911
3912
3913
3914
3915
3916
        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,
3917
                alpha=scale,
3918
            ).view(*output_size)
3919
3920
3921
3922
3923
3924
3925

        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]
            )
3926
            matmul_result = matmul_result.view(*output_size) + core_attention_bias
3927
            matmul_result *= scale
3928

3929
3930
3931
3932
        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":
3933
3934
                _, core_attention_bias = dpa_utils.get_alibi(
                    _alibi_cache,
3935
3936
3937
                    output_size[1],
                    output_size[2],
                    output_size[3],
3938
3939
                    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,
3940
3941
                    alibi_slopes=alibi_slopes,
                    bottom_right_alignment=attn_mask_type not in ["causal", "padding_causal"],
3942
                )
3943
3944
3945
3946
3947
            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,
3948
                alpha=scale,
3949
            )
3950
3951
            matmul_result = (matmul_result.view(*output_size) + core_attention_bias).to(
                dtype=query_layer.dtype
3952
            )
3953
3954
3955

        # attention scores and attention mask [b, np, sq, sk]
        softmax_scale = self.layer_number if apply_qk_layer_scaling else None
3956
        attention_probs = self.scale_mask_softmax(
3957
            matmul_result, attention_mask, attn_mask_type, softmax_scale
3958
        )
3959

3960
3961
3962
3963
3964
        # 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)

3965
3966
3967
3968
3969
3970
3971
3972
3973
3974
3975
3976
3977
3978
3979
        # 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]
3980
        value_layer = value_layer.reshape(value_layer.size(0), output_size[0] * output_size[1], -1)
3981
3982

        # change view [b * np, sq, sk]
3983
        attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
3984
3985
3986
3987
3988
3989
3990

        # 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)

3991
        if q_format == "sbhd":
3992
3993
            # [b, np, sq, hn] --> [sq, b, np, hn]
            context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
3994

3995
3996
3997
            # [sq, b, np, hn] --> [sq, b, hp]
            context_layer = context_layer.view(seqlen, batch_size, -1)

3998
        if q_format == "bshd":
3999
4000
4001
4002
4003
            # [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)
4004

4005
4006
4007
4008
4009
4010
4011
4012
4013
4014
4015
4016
4017
4018
        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)

4019
4020
4021
4022
4023
        return context_layer


class _PrepareQKVForFA(torch.autograd.Function):
    """This class converts QKV from interleaved (s, b, ...) layout
4024
    to separate contiguous q, k, v tensors in (b, s, ...) layout."""
4025
4026

    @staticmethod
4027
4028
4029
4030
    def forward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
4031
        value_layer: torch.Tensor,
4032
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
4033
        # pylint: disable=missing-function-docstring
4034
4035
4036
4037
4038
4039
4040
4041
4042
4043
4044
        # 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
4045
4046
4047
4048
    def backward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        dq: torch.Tensor,
        dk: torch.Tensor,
4049
        dv: torch.Tensor,
4050
    ) -> Tuple[Union[torch.Tensor, None], ...]:
4051
        # pylint: disable=missing-function-docstring
4052
4053
4054
4055
        dqkv = tex.fa_prepare_bwd(dq, dk, dv)
        dq, dk, dv = split_tensor_along_dim(dqkv, -1, 3)
        return dq, dk, dv

4056

4057
class FlashAttention(torch.nn.Module):
4058
    """Dot product attention, using HazyResearch flash-attn package:
4059
    https://github.com/Dao-AILab/flash-attention
4060
4061
4062
4063
    """

    def __init__(
        self,
4064
        softmax_scale: float,
4065
4066
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
4067
4068
        attention_type: str = "self",
        layer_number: Optional[int] = None,
4069
        deterministic: bool = False,
4070
4071
4072
    ) -> None:
        super().__init__()

4073
        if fa_utils.is_installed:
4074
            assert (
4075
4076
                fa_utils.version >= fa_utils.version_required
            ), f"FlashAttention minimum version {fa_utils.version_required} is required."
4077
            assert (
4078
4079
                fa_utils.version <= fa_utils.max_version
            ), f"FlashAttention maximum version {fa_utils.max_version} is supported."
4080

4081
        self.softmax_scale = softmax_scale
4082
4083
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_dropout = attention_dropout
4084
4085
        self.attention_type = attention_type
        self.layer_number = 1 if layer_number is None else layer_number
4086
        self.deterministic = deterministic
4087
        self.logger = logging.getLogger("FlashAttention")
4088
        self.logger.setLevel(attn_log._log_level)
4089
        if not self.logger.hasHandlers():
4090
            self.logger.addHandler(attn_log._stream_handler)
4091
4092
4093
4094
4095
4096

    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
4097
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
4098
4099
4100
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
4101
4102
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
4103
        attn_mask_type: str = "causal",
4104
        window_size: Optional[Tuple[int, int]] = None,
4105
        alibi_slopes: Optional[torch.Tensor] = None,
4106
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
4107
        cp_global_ranks: List[int] = None,
4108
        cp_stream: torch.cuda.Stream = None,
4109
        cp_comm_type: str = "p2p",
4110
4111
        fp8: bool = False,
        fp8_meta: Optional[Dict[str, Any]] = None,
4112
        quantizers=None,
4113
4114
        inference_params: Optional[InferenceParams] = None,
        flash_attention_backend: Optional[PkgVersion] = PkgVersion("0"),
4115
4116
4117
    ) -> torch.Tensor:
        """flash-attn fprop"""

4118
4119
4120
4121
        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."
4122
4123
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
4124
        ), "FlashAttention currently only supports CUDA tensors."
4125
4126
        assert (
            qkv_layout in QKVLayouts
4127
        ), f"FlashAttention does not support qkv_layout = {qkv_layout}!"
4128

4129
4130
4131
4132
4133
4134
        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)
4135
        context_parallel = cp_size > 1
4136

4137
4138
        # 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)
4139

4140
        # convert q, k, v to bshd if they are in sbhd; qkv_format doesn't change
4141
4142
4143
4144
4145
4146
4147
4148
4149
4150
4151
4152
4153
        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 = [
4154
4155
                        x.transpose(0, 1).contiguous()
                        for x in (query_layer, key_layer, value_layer)
4156
                    ]
4157
4158
            elif q_format == "sbhd" and kv_format == "bshd":
                query_layer = query_layer.transpose(0, 1).contiguous()
4159
            if context_parallel:
4160
                query_layer, key_layer, value_layer = [
4161
4162
4163
4164
4165
                    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 = [
4166
                    x.transpose(0, 1).contiguous()
4167
4168
                    for x in (query_layer._data, key_layer._data, value_layer._data)
                ]
4169
                query_layer, key_layer, value_layer = [
4170
                    Float8Tensor.make_like(x, data=x._data, shape=x._data.shape)
4171
4172
                    for x in (query_layer, key_layer, value_layer)
                ]
4173
4174
4175
4176
4177
            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
                )
4178
            if context_parallel:
4179
4180
                query_layer._data, key_layer._data, value_layer._data = [
                    x.contiguous() for x in (query_layer._data, key_layer._data, value_layer._data)
4181
                ]
4182

4183
4184
4185
4186
4187
4188
4189
4190
        # 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
4191

4192
4193
4194
4195
                if "padding" in attn_mask_type:
                    assert (
                        not context_parallel
                    ), "Padding mask not supported with context parallelism!"
4196

4197
4198
4199
4200
4201
                    # [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]
                    ]
4202

4203
                    if self.attention_type == "self":
4204
                        assert (
4205
4206
4207
4208
4209
4210
4211
4212
4213
4214
4215
4216
4217
4218
                            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
4219
                        )
4220
                    else:
4221
4222
4223
4224
4225
4226
4227
4228
4229
4230
4231
4232
4233
4234
4235
4236
4237
                        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
                        )
4238
                else:
4239
4240
4241
4242
4243
4244
                    # 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,
4245
                        )
4246
4247
4248
4249
4250
                    if cu_seqlens_kv is None:
                        cu_seqlens_kv = dpa_utils.get_full_cu_seqlens(
                            batch_size,
                            max_seqlen_kv,
                            key_layer.device,
4251
                        )
4252
4253
4254
4255
4256
4257
4258
4259
4260
4261
4262
4263
4264
4265
4266
4267
4268
4269
4270
4271
4272
4273
4274
            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,
4275
                    )
4276
4277
                    query_layer = Float8Tensor.make_like(
                        query_layer, data=query_layer._data, shape=query_layer._data.shape
4278
                    )
4279
4280
4281
4282
4283
                else:
                    query_layer = tex.convert_bshd_to_thd(
                        query_layer,
                        cu_seqlens_q,
                        batch_size * context_len,
4284
                    )
4285

4286
4287
4288
        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
4289
4290
4291
        if context_parallel and all(
            not isinstance(x, Float8Tensor) for x in [query_layer, key_layer, value_layer]
        ):
4292
4293
4294
            assert (
                alibi_slopes is None
            ), "Alibi slope bias addition is not supported with context parallelism."
4295
            with self.attention_dropout_ctx():
4296
                output = attn_forward_func_with_cp(
4297
4298
4299
4300
4301
4302
4303
4304
                    self.training,
                    query_layer,
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
4305
4306
                    cu_seqlens_q if qkv_format == "thd" else None,
                    cu_seqlens_kv if qkv_format == "thd" else None,
4307
                    self.attention_dropout if self.training else 0.0,
4308
4309
4310
                    cp_group,
                    cp_global_ranks,
                    cp_stream,
4311
                    cp_comm_type,
4312
                    softmax_scale=self.softmax_scale,
4313
                    qkv_format="bshd" if qkv_format == "sbhd" else qkv_format,
4314
                    attn_mask_type=attn_mask_type,
4315
                    deterministic=self.deterministic,
4316
                    window_size=window_size,
4317
                    quantizers=quantizers,
4318
                    pad_between_seqs=False,
4319
                    use_flash_attn_3=use_flash_attn_3,
4320
4321
                )
        else:
4322
4323

            from .cpu_offload import CPUOffloadEnabled
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:
4329
                        set_offloading_param(tensor, "activation_offloading", True)
4330

4331
            with self.attention_dropout_ctx():
4332
4333
4334
4335
4336
4337
4338
4339
4340
4341
4342
4343
                #       | 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
4344
4345
4346
                fa_optional_forward_args_thd = []
                if qkv_format in ["bshd", "sbhd"] and "padding" not in attn_mask_type:
                    func = (
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
4385
4386
                        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,
4387
                    )
4388
                else:
4389
4390
                    fa_3_optional_forward_kwargs = {}
                    fa_3_optional_forward_kwargs["window_size"] = window_size
4391
4392
4393
4394
4395
4396
4397
4398
4399
4400
4401
4402
                    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]
                            )
4403
                    if fp8:
4404
                        QKV_quantizer = quantizers["scaling_fwd"][META_QKV]
4405
                        torch_dtype = get_fp8_torch_dtype(fp8_meta["recipe"], fprop_tensor=True)
4406
                        torch_orig_dtype = query_layer.dtype
4407
4408
4409
4410
4411
4412
4413
4414
4415
4416
4417

                        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

4418
4419
4420
4421
4422
                        # "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."
4423
                        if not isinstance(query_layer, Float8Tensor):
4424
                            query_layer, key_layer, value_layer = (
4425
                                QKV_quantizer(x) for x in [query_layer, key_layer, value_layer]
4426
                            )
4427
4428
4429
4430
                        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)
4431
                        )
4432
                        fa_3_optional_forward_kwargs["k_descale"] = key_layer._scale_inv.unsqueeze(
4433
                            0
4434
4435
4436
                        ).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)
4437
                        )
4438
4439
4440
                        query_layer, key_layer, value_layer = (
                            convert_to_torch_float8(x, torch_dtype)
                            for x in [query_layer, key_layer, value_layer]
4441
                        )
4442
                    try:
4443
                        output = func(
4444
4445
4446
4447
4448
4449
4450
4451
                            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,
                        )
4452
4453
                        if isinstance(output, (List, Tuple)):
                            output = output[0]
4454
                    except TypeError as e:
4455
                        if fa_utils.v3_0_0_beta:
4456
4457
4458
4459
                            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"
4460
                                + fa_utils.v3_installation_steps,
4461
4462
4463
4464
4465
4466
4467
4468
                            ) + 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)
4469

4470
4471
4472
4473
4474
4475
4476
4477
4478
4479
4480
4481
4482
4483
4484
4485
4486
4487
4488
4489
4490
        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,
                )
4491

4492
        if q_format == "sbhd":
4493
4494
4495
4496
4497
4498
4499
4500
4501
4502
4503
4504
4505
4506
            # (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)
4507
        elif q_format == "bshd":
4508
4509
            # (bs)hd -> bs(hd)
            output = output.reshape(batch_size, max_seqlen_q // cp_size, -1)
4510
        elif q_format == "thd":
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
4541
4542
            # 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
4543
4544
        )

4545
4546
    return combined_tensor

4547

4548
4549
4550
4551
class FusedAttnFunc(torch.autograd.Function):
    """Function for FusedAttention with separate Q, K, V tensors"""

    @staticmethod
4552
4553
4554
4555
4556
4557
4558
    def forward(
        ctx,
        is_training,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q,
        cu_seqlens_kv,
4559
4560
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
4561
4562
        page_table_k,
        page_table_v,
4563
4564
4565
4566
4567
4568
4569
4570
4571
4572
        q,
        k,
        v,
        attn_bias,
        attn_scale,
        dropout_p,
        fast_zero_fill,
        qkv_layout,
        attn_bias_type,
        attn_mask_type,
4573
        window_size,
4574
4575
4576
4577
4578
        rng_gen,
        fused_attention_backend,
        use_FAv2_bwd,
        fp8,
        fp8_meta,
4579
        quantizers,
4580
        deterministic,
4581
    ):
4582
        # pylint: disable=missing-function-docstring
4583
        # "fp8_mha" decides outputs in fp8, while inputs are inferred from the real dtype
4584
        is_input_fp8 = False
4585
        is_output_fp8 = fp8_meta["recipe"].fp8_mha if "recipe" in fp8_meta else False
4586
4587
4588
4589

        # 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
4590
4591
4592
        fake_dtype = q.dtype

        QKV_quantizer, O_quantizer, S_quantizer, dQKV_quantizer, dO_quantizer, dP_quantizer = (
4593
            dpa_utils.get_attention_quantizers(fp8, quantizers, cp_specific_quantizers=False)
4594
        )
4595
4596
        if fp8:
            fused_attention_backend = FusedAttnBackend["FP8"]
4597
4598
4599
            assert isinstance(k, q.__class__) and isinstance(
                v, q.__class__
            ), "q, k, and v must have the same type."
4600

4601
            is_input_fp8 = isinstance(q, Float8Tensor)
4602
            q_fp8, k_fp8, v_fp8 = None, None, None
4603
            if is_input_fp8:
4604
                q_fp8, k_fp8, v_fp8 = q, k, v
4605
4606
            else:
                # 1: qkv packed, 2: kv packed, 3: qkv separate
4607
                qkv_group = len(qkv_layout.replace("paged_kv_", "").split("_"))
4608
4609
4610
4611
4612
4613
4614
4615
4616
4617
4618
4619
4620
4621
4622
4623
4624
4625
4626
4627
                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
4628
            # q_fp8, k_fp8, v_fp8, out_fp8: torch.float8_e4m3fn
4629
            out_fp8, aux_ctx_tensors = fused_attn_fwd(
4630
4631
4632
4633
4634
4635
4636
4637
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q_fp8,
                k_fp8,
                v_fp8,
4638
                fake_dtype,
4639
4640
                fused_attention_backend,
                attn_bias,
4641
4642
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
4643
4644
                None,
                None,
4645
4646
                S_quantizer,
                O_quantizer,
4647
4648
4649
4650
4651
4652
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
4653
                window_size,
4654
4655
                rng_gen,
            )
4656
            if is_output_fp8:
4657
                out_ret = out_fp8
4658
            else:
4659
                out_ret = out_fp8.dequantize().view(out_fp8.shape)
4660
4661
            # is_output_fp8 = False: out_save.dtype = torch.float16 or torch.bfloat16
            # is_output_fp8 = True:  out_save.dtype = torch.float8_e4m3fn
4662
4663
            out_save = out_ret

4664
            if not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
4665
                # 1: qkv packed, 2: kv packed, 3: qkv separate
4666
                if is_input_fp8:
4667
                    qkv_group = len(qkv_layout.replace("paged_kv_", "").split("_"))
4668
4669
4670
4671
                    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])
4672
4673
                        qkv_no_fp8 = qkv_c.dequantize().view(qkv.shape)
                        q, k, v = _SplitAlongDim.apply(qkv_no_fp8, dim, [1, 1, 1], True)
4674
                    if qkv_group == 2:
4675
                        q = q.dequantize()
4676
                        dim = qkv_layout.replace("paged_kv_", "").split("_")[1].find("2")
4677
4678
                        kv = _combine_tensors([k, v], dim)
                        kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
4679
4680
                        kv_no_fp8 = kv.dequantize()
                        k, v = _SplitAlongDim.apply(kv_no_fp8, dim, [1, 1], True)
4681
                    if qkv_group == 3:
4682
4683
4684
                        q = q.dequantize()
                        k = k.dequantize()
                        v = v.dequantize()
4685
                if is_output_fp8:
4686
4687
4688
                    out_save = out_fp8.dequantize()

            fp8_tensors = (q_fp8, k_fp8, v_fp8, out_fp8)
4689
        else:
4690
            # q, k, v, out_ret: torch.float16 or torch.bfloat16
4691
            out_ret, aux_ctx_tensors = fused_attn_fwd(
4692
4693
4694
4695
4696
4697
4698
4699
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q,
                k,
                v,
4700
                fake_dtype,
4701
4702
                fused_attention_backend,
                attn_bias,
4703
4704
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
4705
4706
                page_table_k,
                page_table_v,
4707
4708
                None,  # s_quantizer
                None,  # o_quantizer
4709
4710
4711
4712
4713
4714
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
4715
                window_size,
4716
4717
                rng_gen,
            )
4718
            out_save = out_ret
4719
            fp8_tensors = (None, None, None, None)
4720

4721
4722
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))

4723
        from .cpu_offload import CPUOffloadEnabled
4724

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

4731
            qkv_layout = "sbhd_sbhd_sbhd"
4732
4733
            for tensor in tensor_list:
                if tensor is not None:
4734
4735
4736
4737
4738
                    set_offloading_param(tensor, "activation_offloading", True)

            for tensor in aux_ctx_tensors:
                if tensor is not None:
                    set_offloading_param(tensor, "activation_offloading", True)
4739

4740
4741
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
4742
        qkvo_tensors = (q, k, v, out_save) if not ctx.fp8 else (None, None, None, None)
4743
4744
        tensors_to_save, tensor_objects = prepare_for_saving(
            *fp8_tensors,
4745
4746
4747
            *qkvo_tensors,
            cu_seqlens_q,
            cu_seqlens_kv,
4748
4749
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
4750
4751
            *aux_ctx_tensors,
        )
4752
4753
        ctx.save_for_backward(*tensors_to_save)
        ctx.tensor_objects = tensor_objects
4754
        ctx.fp8_meta = fp8_meta
4755
4756
4757
4758
4759

        ctx.dQKV_quantizer = dQKV_quantizer
        ctx.dO_quantizer = dO_quantizer
        ctx.dP_quantizer = dP_quantizer
        ctx.S_quantizer = S_quantizer
4760
4761
4762
        if ctx.fp8:
            ctx.S_quantizer = S_quantizer.copy()
            ctx.S_quantizer.scale = S_quantizer.scale.clone()
4763

4764
4765
4766
4767
4768
4769
4770
4771
        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
4772
        ctx.window_size = window_size
4773
        ctx.fused_attention_backend = (
4774
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
4775
        )
4776
        ctx.use_FAv2_bwd = use_FAv2_bwd
4777
        ctx.deterministic = deterministic
4778

4779
        return out_ret
4780
4781
4782

    @staticmethod
    def backward(ctx, d_out):
4783
        # pylint: disable=missing-function-docstring
4784
        if ctx.is_output_fp8:
4785
4786
4787
            assert isinstance(
                d_out, Float8Tensor
            ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
4788

4789
4790
4791
4792
4793
        # 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

4794
        d_out = d_out.contiguous()
4795
        (
4796
4797
4798
4799
            q_fp8,
            k_fp8,
            v_fp8,
            out_fp8,
4800
4801
4802
4803
4804
4805
            q,
            k,
            v,
            out,
            cu_seqlens_q,
            cu_seqlens_kv,
4806
4807
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
4808
4809
4810
4811
4812
            *other_tensors,
        ) = restore_from_saved(ctx.tensor_objects, ctx.saved_tensors)

        aux_ctx_tensors = other_tensors

4813
4814
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
4815
        rest = [None]
4816
        if ctx.use_FAv2_bwd:
4817
            softmax_lse, rng_state = aux_ctx_tensors
4818
4819
4820
            dq = torch.empty_like(q)
            dk = torch.empty_like(k)
            dv = torch.empty_like(v)
4821
            d_out, q, k, v, out = [maybe_contiguous(x) for x in (d_out, q, k, v, out)]
4822
            flash_attn_cuda_bwd(
4823
4824
4825
4826
4827
4828
4829
4830
4831
4832
4833
4834
4835
4836
4837
4838
4839
4840
4841
                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,
4842
            )
4843
4844
4845
            dq = dq[..., : d_out.shape[-1]]
            dk = dk[..., : d_out.shape[-1]]
            dv = dv[..., : d_out.shape[-1]]
4846
        else:
4847
4848
            with torch.cuda.nvtx.range("_FusedAttn"):
                if ctx.fp8:
4849
                    if ctx.is_output_fp8:
4850
4851
                        d_out_fp8 = d_out
                    else:
4852
                        d_out_fp8 = ctx.dO_quantizer(d_out)
4853
4854
4855
                    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
4856
                    dq_fp8, dk_fp8, dv_fp8, *rest = fused_attn_bwd(
4857
4858
4859
4860
4861
4862
4863
4864
4865
                        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,
4866
4867
                        fake_dtype,
                        dqkv_dtype,
4868
                        aux_ctx_tensors,
4869
                        ctx.fused_attention_backend,
4870
4871
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
4872
4873
4874
                        ctx.S_quantizer,
                        ctx.dP_quantizer,
                        ctx.dQKV_quantizer,
4875
4876
4877
4878
4879
4880
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
4881
4882
                        ctx.window_size,
                        ctx.deterministic,
4883
                    )
4884

4885
4886
                    # is_input_fp8 = False: dq, dk, dv: torch.float16 or torch.bfloat16
                    # is_input_fp8 = True:  dq, dk, dv: torch.float8_e5m2
4887
                    if not ctx.is_input_fp8:
4888
                        qkv_group = len(ctx.qkv_layout.replace("paged_kv_", "").split("_"))
4889
                        if qkv_group == 1:
4890
                            dim = ctx.qkv_layout.find("3")
4891
4892
                            dqkv_fp8_data = _combine_tensors(
                                [dq_fp8._data, dk_fp8._data, dv_fp8._data], dim
4893
                            )
4894
4895
4896
4897
4898
                            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)
4899
                        if qkv_group == 2:
4900
                            dq = dq_fp8.dequantize()
4901
4902
4903
4904
4905
                            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]
                            )
4906
4907
                            dkv = dkv_c_fp8.dequantize()
                            dk, dv = _SplitAlongDim.apply(dkv, dim, [1, 1], True)
4908
                        if qkv_group == 3:
4909
4910
4911
4912
4913
                            dq = dq_fp8.dequantize()
                            dk = dk_fp8.dequantize()
                            dv = dv_fp8.dequantize()
                    else:
                        dq, dk, dv = dq_fp8, dk_fp8, dv_fp8
4914
                else:
4915
4916
                    if isinstance(d_out, QuantizedTensor):
                        d_out = d_out.dequantize()
4917
4918
                    dqkv_dtype = TE_DType[d_out.dtype]
                    # q, k, v, out, d_out, dq, dk, dv: torch.float16 or torch.bfloat16
4919
                    dq, dk, dv, *rest = fused_attn_bwd(
4920
4921
4922
4923
4924
4925
4926
4927
4928
                        ctx.max_seqlen_q,
                        ctx.max_seqlen_kv,
                        cu_seqlens_q,
                        cu_seqlens_kv,
                        q,
                        k,
                        v,
                        out,
                        d_out,
4929
4930
                        fake_dtype,
                        dqkv_dtype,
4931
                        aux_ctx_tensors,
4932
                        ctx.fused_attention_backend,
4933
4934
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
4935
4936
4937
4938
4939
4940
4941
4942
4943
                        None,
                        None,
                        None,
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
4944
4945
                        ctx.window_size,
                        ctx.deterministic,
4946
                    )
4947

4948
4949
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
4950
4951
4952
4953
4954
4955
4956
4957
            return (
                None,
                None,
                None,
                None,
                None,
                None,
                None,
4958
4959
                None,
                None,
4960
4961
4962
4963
4964
4965
4966
4967
4968
4969
4970
4971
4972
4973
4974
4975
4976
4977
4978
                dq,
                dk,
                dv,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
            )
4979
        # else, return (dqkv, dbias)
4980
4981
4982
4983
4984
4985
4986
4987
        return (
            None,
            None,
            None,
            None,
            None,
            None,
            None,
4988
4989
            None,
            None,
4990
4991
4992
4993
4994
4995
4996
4997
4998
4999
5000
5001
5002
5003
5004
5005
5006
            dq,
            dk,
            dv,
            rest[0],
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
5007
            None,
5008
        )
5009

5010

5011
class FusedAttention(torch.nn.Module):
5012
5013
5014
5015
5016
5017
5018
5019
5020
    """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:

5021
5022
5023
5024
    | backend       | 1                       | 2                              |
    | flash based   | no                      | yes                            |
    | cuDNN based   | yes                     | yes                            |
    | qkv dtype     | fp16/bf16               | fp16/bf16                      |
5025
    | attn_type     | self/cross              | self/cross                     |
5026
    | qkv_layout    |                         |                                |
5027
    |  - (q,k,v)    | sb3hd, bs3hd            | sb3hd, bs3hd, sbh3d, bsh3d     |
5028
    |               | sbhd_sb2hd, bshd_bs2hd  | sbhd_sb2hd, bshd_bs2hd         |
5029
5030
    |               | bshd_bshd_bshd          | sbhd_sbh2d, bshd_bsh2d         |
    |               |                         | sbhd_sbhd_sbhd, bshd_bshd_bshd |
5031
5032
    | mask_type     | causal/padding/no_mask  | causal/padding/no_mask         |
    | bias_type     | post_scale_bias/no_bias | post_scale_bias/alibi/no_bias  |
5033
    | dropout       | yes                     | yes                            |
5034
5035
    | max_seqlen    | <=512, multiple of 64   | any, multiple of 64            |
    | head_dim      | 64                      | <=128, multiple of 8           |
5036
    | output dtype  | fp16/bf16               | fp16/bf16                      |
5037
5038
5039
5040
    """

    def __init__(
        self,
5041
        softmax_scale: float,
5042
5043
5044
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
        attention_type: str = "self",
5045
5046
        layer_number: Optional[int] = None,
        deterministic: bool = False,
5047
5048
5049
    ) -> None:
        super().__init__()

5050
        self.softmax_scale = softmax_scale
5051
5052
5053
        self.attention_dropout = attention_dropout
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_type = attention_type
5054
5055
5056
        self.use_FAv2_bwd = os.getenv(
            "NVTE_FUSED_ATTN_USE_FAv2_BWD", "0"
        ) == "1" and get_device_compute_capability() == (9, 0)
5057
        self.layer_number = 1 if layer_number is None else layer_number
5058
        self.deterministic = deterministic
5059

5060
        def remove_extra_states_check(self, incompatible_keys):  # pylint: disable=unused-argument
5061
5062
            """
            Temporarily remove fused_attention._extra_state as a missing key
5063
            or an unexpected key when loading Transformer Engine checkpoints.
5064
5065
            Please store FP8 metadata as DotProductAttention's _extra_state,
            rather than FusedAttention's _extra_state. This hook will be
5066
            phased out in Transformer Engine 2.0.
5067
5068
            """
            for key in incompatible_keys.missing_keys:
5069
                if "fused_attention._extra_state" in key:
5070
                    incompatible_keys.missing_keys.remove(key)
5071
5072
5073
5074
5075
5076
5077
            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."
                    )
5078

5079
5080
        self.register_load_state_dict_post_hook(remove_extra_states_check)

5081
    @no_torch_dynamo()
5082
5083
5084
5085
5086
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
5087
5088
5089
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
5090
5091
        cu_seqlens_q_padded: Optional[torch.Tensor] = None,
        cu_seqlens_kv_padded: Optional[torch.Tensor] = None,
5092
5093
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
5094
        attn_mask_type: str = "causal",
5095
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
5096
        window_size: Optional[Tuple[int, int]] = None,
5097
        fused_attention_backend: tex.NVTE_Fused_Attn_Backend = tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend,
5098
5099
5100
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
        fast_zero_fill: bool = True,
5101
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
5102
5103
        cp_global_ranks: List[int] = None,
        cp_stream: torch.cuda.Stream = None,
5104
        cp_comm_type: str = "p2p",
5105
5106
        fp8: bool = False,
        fp8_meta: Optional[Dict[str, Any]] = None,
5107
        quantizers=None,
5108
        pad_between_seqs: bool = False,
5109
        inference_params: Optional[InferenceParams] = None,
5110
5111
    ) -> torch.Tensor:
        """fused attention fprop"""
5112
5113
5114
        assert (
            fused_attention_backend != tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend
        ), "No fused attention backend supports this input combination!"
5115
5116
5117
5118
        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."
5119
5120
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
5121
        ), "FusedAttention only supports CUDA tensors."
5122
5123
        assert (
            qkv_layout in QKVLayouts
5124
        ), f"FusedAttention does not support qkv_layout = {qkv_layout}!"
5125

5126
5127
5128
5129
5130
5131
        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)
5132
        context_parallel = cp_size > 1
5133

5134
5135
        # 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)
5136

5137
5138
5139
5140
5141
5142
5143
5144
5145
5146
        # 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]

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
5177
5178
5179
5180
        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,
5181
                        )
5182
5183
5184
5185
5186
5187
5188
5189
5190
5191
5192
5193
5194
5195
5196
5197
5198
                    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:
5199
            cu_seqlens_q_padded = cu_seqlens_q
5200
        if (kv_format == "thd" or "padding" in attn_mask_type) and cu_seqlens_kv_padded is None:
5201
            cu_seqlens_kv_padded = cu_seqlens_kv
5202

5203
5204
5205
5206
5207
        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)
        )
5208

5209
5210
5211
5212
5213
5214
5215
5216
5217
5218
5219
        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!"
            )

5220
        if context_parallel:
5221
            assert (
5222
5223
                fp8
                or fused_attention_backend == tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen
5224
5225
5226
5227
5228
5229
5230
            ), 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)
            ]
5231
5232
5233
            with self.attention_dropout_ctx():
                output = attn_forward_func_with_cp(
                    self.training,
5234
5235
5236
5237
5238
5239
5240
                    query_layer,
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
5241
5242
                    cu_seqlens_q_padded,
                    cu_seqlens_kv_padded,
5243
                    self.attention_dropout if self.training else 0.0,
5244
5245
5246
                    cp_group,
                    cp_global_ranks,
                    cp_stream,
5247
                    cp_comm_type,
5248
                    softmax_scale=self.softmax_scale,
5249
                    qkv_format=qkv_format,
5250
                    attn_mask_type=attn_mask_type,
5251
5252
                    attn_bias_type=core_attention_bias_type,
                    attn_bias=core_attention_bias,
5253
                    deterministic=self.deterministic,
5254
                    use_fused_attention=True,
5255
                    window_size=window_size,
5256
5257
                    fp8=fp8,
                    fp8_meta=fp8_meta,
5258
                    quantizers=quantizers,
5259
                    pad_between_seqs=pad_between_seqs,
5260
5261
                )
        else:
5262
5263
5264
5265
5266
5267
5268
            with self.attention_dropout_ctx():
                output = FusedAttnFunc.apply(
                    self.training,
                    max_seqlen_q,
                    max_seqlen_kv,
                    cu_seqlens_q,
                    cu_seqlens_kv,
5269
5270
                    cu_seqlens_q_padded,
                    cu_seqlens_kv_padded,
5271
5272
                    page_table,
                    page_table,
5273
5274
5275
5276
5277
5278
5279
5280
5281
5282
                    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,
5283
                    window_size,
5284
5285
5286
5287
5288
                    None,  # rng_gen
                    fused_attention_backend,
                    use_FAv2_bwd,
                    fp8,
                    fp8_meta,
5289
                    quantizers,
5290
                    self.deterministic,
5291
                )
5292

5293
5294
        # ...hd -> ...(hd)
        return output.view(*output.shape[:-2], -1)
5295
5296


5297
class DotProductAttention(TransformerEngineBaseModule):
5298
5299
5300
5301
5302
5303
    """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::

5304
        Argument :attr:`attention_mask` in the `forward` call is only used when
5305
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
5306
5307
5308

    .. warning::

5309
        FlashAttention uses a non-deterministic algorithm for optimal performance. To observe
5310
        deterministic behavior at the cost of performance, use FlashAttention version >= `2.4.1`
5311
5312
        and set the environment variable :attr:`NVTE_ALLOW_NONDETERMINISTIC_ALGO=0`. In order
        to disable`flash-attn` entirely, set :attr:`NVTE_FLASH_ATTN=0`.
5313

5314
5315
5316
5317
5318
5319
5320
    .. 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>`_).


5321
5322
5323
5324
    Parameters
    ----------
    num_attention_heads : int
                         number of attention heads in the transformer layer.
5325
5326
5327
    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.
5328
5329
5330
5331
5332
5333
5334
5335
    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`.
5336
5337
    attention_dropout: float, default = 0.0
                      dropout probability for the dropout op during multi-head attention.
5338
    attn_mask_type: str, default = `causal`
5339
                   type of attention mask passed into softmax operation, options are "`no_mask`",
5340
5341
5342
5343
5344
5345
5346
5347
5348
                   "`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
5349
                   "`padding_causal`" and "`padding_causal_bottom_right`", Transformer Engine
5350
5351
5352
5353
5354
5355
5356
5357
5358
5359
5360
5361
5362
5363
                   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].
5364
5365
5366
5367
    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
5368
5369
5370
                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
5371
                be overridden by :attr:`window_size` in `forward` as well.
5372
5373
    attention_type: str, default = `self`
                   type of attention, either "`self`" and "`cross`".
5374
5375
5376
    layer_number: int, default = `None`
                 layer number of the current `DotProductAttention` when multiple such modules
                 are concatenated, for instance in consecutive transformer blocks.
5377
5378
5379
    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,
5380
               `h` the number of heads, `d` head size, and `t` the total number of tokens
5381
5382
5383
5384
5385
               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.
5386
               For that, please use `get_qkv_layout` to gain the layout information.
5387
5388
    softmax_scale: Optional[float], default = `None`
                softmax scale for the attention scores. If `None`, defaults to
5389
                `1.0/math.sqrt(kv_channels if isinstance(kv_channels, int) else kv_channels[0])`.
5390
5391
5392
5393
5394
5395
5396
5397
5398

    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.
5399
    cp_group : Union[ProcessGroup, List[ProcessGroup]], default = `None`
5400
              context parallel process group.
5401
5402
5403
              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.
5404
5405
5406
5407
5408
5409
5410
    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.
5411
    cp_comm_type : str, default = `p2p`
5412
                  inter-gpu communication type for context parallelism.
5413
                  Can be "p2p" or "all_gather" or "a2a" or "a2a+p2p".
5414
5415
5416
5417
5418
5419
                  "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.
5420
5421
5422
                  "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).
5423
5424
5425
5426
5427
    """

    def __init__(
        self,
        num_attention_heads: int,
5428
        kv_channels: Union[int, Tuple[int, int]],
5429
        num_gqa_groups: Optional[int] = None,
5430
        attention_dropout: float = 0.0,
5431
        qkv_format: str = "sbhd",
5432
        attn_mask_type: str = "causal",
5433
        window_size: Optional[Tuple[int, int]] = None,
5434
5435
5436
5437
5438
        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,
5439
        attention_type: str = "self",
5440
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
5441
        cp_global_ranks: List[int] = None,
5442
        cp_stream: torch.cuda.Stream = None,
5443
        cp_comm_type: str = "p2p",
5444
        softmax_scale: Optional[float] = None,
5445
5446
5447
    ) -> None:
        super().__init__()

5448
        self.logger = logging.getLogger("DotProductAttention")
5449
        self.logger.setLevel(attn_log._log_level)
5450
        if not self.logger.hasHandlers():
5451
            self.logger.addHandler(attn_log._stream_handler)
5452
        self.qkv_format = qkv_format
5453
        attn_mask_type = attn_mask_type.replace(",", "_")
5454
5455
        if attn_mask_type == "causal_padding":
            attn_mask_type = "padding_causal"
5456
        self.attn_mask_type = attn_mask_type
5457
        self.window_size = dpa_utils.check_set_window_size(attn_mask_type, window_size)
5458
5459
5460
5461
5462
5463
5464
        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)
5465
        self.get_rng_state_tracker = get_rng_state_tracker
5466
        self.num_attention_heads = num_attention_heads
5467
        self.layer_number = 1 if layer_number is None else layer_number
5468
5469
5470
        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream
5471
        self.cp_comm_type = cp_comm_type
5472

5473
5474
5475
5476
5477
5478
        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]
        )
5479

5480
        self.num_gqa_groups = num_attention_heads if num_gqa_groups is None else num_gqa_groups
5481
        self.num_gqa_groups_per_partition = int(self.num_gqa_groups // self.tp_size)
5482

5483
5484
5485
        assert (
            num_attention_heads % self.num_gqa_groups == 0
        ), "The number of attention heads must be divisible by the number of GQA groups!"
5486

5487
        self.rng_states_tracker = None
5488
5489
5490
        if sequence_parallel or get_rng_state_tracker is None:
            attention_dropout_ctx = nullcontext
        else:
5491
5492
5493
            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
5494

5495
        if softmax_scale is None:
5496
5497
5498
            softmax_scale = 1.0 / math.sqrt(
                kv_channels if isinstance(kv_channels, int) else kv_channels[0]
            )
5499

5500
5501
5502
        self.deterministic = (
            not bool(int(os.getenv("NVTE_ALLOW_NONDETERMINISTIC_ALGO", "1")))
            or torch.are_deterministic_algorithms_enabled()
5503
        )
5504
5505
5506
5507
5508
5509
5510
5511
5512
5513
5514
5515
5516
5517
5518
5519
5520
5521
5522
        # 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"
5523

5524
        assert attention_type in AttnTypes, f"attention_type {attention_type} not supported"
5525
5526
5527
5528

        self.attention_type = attention_type
        self.attention_dropout = attention_dropout

5529
5530
5531
5532
5533
        attn_kwargs = {
            "attention_dropout": attention_dropout,
            "attention_dropout_ctx": attention_dropout_ctx,
        }

5534
5535
5536
5537
5538
5539
5540
        self.flash_attention = FlashAttention(
            softmax_scale,
            attention_type=attention_type,
            layer_number=layer_number,
            deterministic=self.deterministic,
            **attn_kwargs,
        )
5541

5542
        # Instantiating three types since use of flash-attn and FusedAttention
5543
        # might be ruled out due to forward inputs.
5544
5545
5546
5547
5548
5549
5550
        self.fused_attention = FusedAttention(
            softmax_scale,
            attention_type=attention_type,
            layer_number=layer_number,
            deterministic=self.deterministic,
            **attn_kwargs,
        )
5551

5552
        self.unfused_attention = UnfusedDotProductAttention(
5553
5554
5555
5556
            softmax_scale,
            attention_type=attention_type,
            **attn_kwargs,
            layer_number=layer_number,
5557
        )
5558

5559
5560
5561
        def remove_extra_states_check(self, incompatible_keys):  # pylint: disable=unused-argument
            """
            Temporarily remove core_attention._extra_state as a missing key
5562
5563
            when loading older Transformer Engine checkpoints. Will phase out
            this hook in Transformer Engine 2.0.
5564
5565
5566
5567
5568
5569
5570
            """
            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)

5571
5572
5573
5574
5575
5576
5577
5578
5579
5580
5581
5582
5583
5584
5585
5586
5587
5588
5589
5590
5591
5592
    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
        )

5593
5594
5595
5596
    def _checkpointed_attention_forward(
        self,
        attention_func: Callable,
        *forward_args: Tuple[torch.Tensor, ...],
5597
        **forward_kwargs: Dict[str, Any],
5598
5599
5600
    ) -> torch.Tensor:
        """Forward method with activation checkpointing."""

5601
5602
        def custom_forward(*input_args, **input_kwargs):
            return attention_func(*input_args, **input_kwargs)
5603
5604
5605

        hidden_states = checkpoint(
            custom_forward,
5606
5607
5608
            distribute_saved_activations=False,
            get_rng_state_tracker=self.get_rng_state_tracker,
            tp_group=self.tp_group,
5609
            *forward_args,
5610
            **forward_kwargs,
5611
5612
5613
5614
        )

        return hidden_states

5615
5616
    def set_context_parallel_group(
        self,
5617
        cp_group: Union[dist_group_type, List[dist_group_type], None],
5618
5619
        cp_global_ranks: List[int],
        cp_stream: torch.cuda.Stream,
5620
        cp_comm_type: str = "p2p",
5621
    ) -> None:
5622
5623
5624
5625
5626
5627
        """
        Set the context parallel attributes for the given
        module before executing the forward pass.

        Parameters
        ----------
5628
        cp_group : Union[ProcessGroup, List[ProcessGroup]]
5629
                  context parallel process group.
5630
5631
5632
                  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.
5633
5634
5635
5636
        cp_global_ranks : List[int]
                         list of global ranks in the context group.
        cp_stream : torch.cuda.Stream
                   cuda stream for context parallel execution.
5637
        cp_comm_type : str, default = `p2p`
5638
                      inter-gpu communication type for context parallelism.
5639
                      Can be "p2p" or "all_gather" or "a2a" or "a2a+p2p".
5640
5641
5642
5643
5644
5645
                      "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.
5646
5647
5648
                      "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).
5649
        """
5650
5651
5652
        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream
5653
        self.cp_comm_type = cp_comm_type
5654

5655
    @no_torch_dynamo(recursive=False)
5656
5657
5658
5659
5660
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
5661
5662
5663
5664
5665
5666
5667
5668
        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,
5669
        attn_mask_type: Optional[str] = None,
5670
        window_size: Optional[Tuple[int, int]] = None,
5671
        checkpoint_core_attention: bool = False,
5672
5673
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
5674
        alibi_slopes: Optional[torch.Tensor] = None,
5675
        fast_zero_fill: bool = True,
5676
        inference_params: Optional[InferenceParams] = None,
5677
        pad_between_seqs: Optional[bool] = None,
5678
5679
5680
5681
5682
5683
    ) -> torch.Tensor:
        """
        Dot Product Attention Layer.

        .. note::

5684
5685
            Argument :attr:`attention_mask` is only used when :attr:`attn_mask_type`
            includes '"padding"' or `"arbitrary"`.
5686

5687
5688
        .. note::

5689
5690
5691
5692
5693
5694
5695
5696
5697
5698
5699
5700
5701
            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,
5702
            and FusedAttention backend if applicable, to use. Transformer Engine prioritizes
5703
5704
5705
5706
            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
5707
5708
            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
5709
            optimizations in FusedAttention. When unset, Transformer Engine determines the code path
5710
5711
            based on its internal logic. These optimizations trade memory for performance
            and should be used with care.
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
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5740
5741
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5751
5752
5753
5754
5755
5756
5757
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5759
5760
5761
5762
5763
5764
5765
5766
        .. 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`}.

5767
5768
5769
5770
5771
5772
5773
5774
        Parameters
        ----------
        query_layer : torch.Tensor
                     Query tensor.
        key_layer : torch.Tensor
                   Key tensor.
        value_layer : torch.Tensor
                     Value tensor.
5775
5776
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]],
             default = `None`. Boolean tensor(s) used to mask out attention softmax input.
5777
             It should be `None` for causal masks and "`no_mask`". For padding masks, it should be
5778
5779
             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]
5780
5781
5782
5783
             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.
5784
5785
5786
        qkv_format: str, default = `None`
                   If provided, overrides :attr:`qkv_format` from initialization.
        cu_seqlens_q: Optional[torch.Tensor], default = `None`
5787
                   Cumulative sum of sequence lengths (without offset) in a batch for `query_layer`,
5788
                   with shape [batch_size + 1] and dtype torch.int32.
5789
                   See :ref:`note<cu_seqlens note>` for more details.
5790
        cu_seqlens_kv: Optional[torch.Tensor], default = `None`
5791
5792
                   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.
5793
                   See :ref:`note<cu_seqlens note>` for more details.
5794
5795
5796
5797
5798
        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`.
5799
                   See :ref:`note<cu_seqlens note>` for more details.
5800
5801
5802
5803
5804
        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`.
5805
                   See :ref:`note<cu_seqlens note>` for more details.
5806
5807
        max_seqlen_q: Optional[int], default = `None`
                      Maximum sequence length in `query_layer`.
5808
                      See :ref:`note<max_seqlen note>` for more details.
5809
5810
        max_seqlen_kv: Optional[int], default = `None`
                       Maximum sequence length in `key_layer` and `value_layer`.
5811
                       See :ref:`note<max_seqlen note>` for more details.
5812
5813
5814
5815
5816
5817
5818
        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.
5819
        window_size: Optional[Tuple[int, int]], default = `None`
5820
                    Sliding window size for local attention.
5821
5822
5823
5824
5825
        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.
5826
        core_attention_bias_type: str, default = `no_bias`
5827
                    Bias type, {`no_bias`, `pre_scale_bias`, `post_scale_bias`, `alibi`}
5828
        core_attention_bias: Optional[torch.Tensor], default = `None`
5829
5830
                    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.
5831
5832
5833
5834
        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.
5835
        fast_zero_fill: bool, default = `True`
5836
                    Whether to use the fast path to set output tensors to 0 or not.
5837
5838
5839
5840
5841
5842
5843
5844
5845
5846
        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.
5847
5848
5849
        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.
5850
        """
5851

5852
5853
5854
5855
5856
        with self.prepare_forward(
            query_layer,
            num_gemms=3,
            allow_non_contiguous=True,
        ) as query_layer:
5857
5858
5859
5860
5861
5862
5863
5864
5865
5866
            # 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
5867
5868
5869
5870
            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
5871
                        self.logger.warning(
5872
5873
5874
                            """Forcing fp8_meta["recipe"].fp8_dpa=True due to """
                            """fp8_meta["recipe"].fp8_mha=True"""
                        )
5875
5876
5877
5878
5879
5880
5881
5882
5883
5884
            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."""
5885

5886
            # checks for q/k/v shapes
5887
5888
5889
            assert (
                query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
            ), "DotProductAttention only supports CUDA tensors."
5890
5891
5892
            assert (
                query_layer.dtype == key_layer.dtype and query_layer.dtype == value_layer.dtype
            ), "Queries, keys and values must have the same data type!"
5893
5894
5895
            assert (
                key_layer.shape[:-1] == value_layer.shape[:-1]
            ), "Keys and values must have the same batch size, sequence length and number of heads!"
5896
5897
            num_attention_heads = query_layer.shape[-2]
            num_gqa_groups = key_layer.shape[-2]
5898
            assert (
5899
5900
5901
5902
5903
5904
                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}, "
5905
5906
            "but expected head_dim = {self.hidden_size_per_attention_head_k}!"
            assert (
5907
5908
                head_dim_v == self.hidden_size_per_attention_head_v
            ), f"Values have head_dim = {head_dim_v}, "
5909
            "but expected head_dim = {self.hidden_size_per_attention_head_v}!"
5910
5911
5912
5913
            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}."
            )
5914

5915
            # checks for attention mask
5916
5917
5918
5919
5920
5921
            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"
5922
            assert (
5923
5924
                attn_mask_type in AttnMaskTypes
            ), f"Attention mask type {attn_mask_type} is not supported!"
5925

5926
            # checks for sliding window
5927
5928
            if window_size is None:
                window_size = self.window_size
5929
            window_size = dpa_utils.check_set_window_size(attn_mask_type, window_size)
5930

5931
5932
5933
            # checks for qkv_format
            if qkv_format is None:
                qkv_format = self.qkv_format
5934
5935
5936
5937
5938
            assert qkv_format in [
                "sbhd",
                "bshd",
                "thd",
            ], "DotProductAttention only supports qkv_format = {'sbhd', 'bshd', 'thd'}!"
5939
5940
5941
5942
5943
5944
5945
5946
5947
5948
5949
5950
5951
            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
5952
            if qkv_format == "thd":
5953
                assert all(
5954
5955
                    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!"
5956
5957
5958
                assert (
                    "padding" in attn_mask_type
                ), "Attention mask type must be padding or padding_causal for qkv_format=thd!"
5959
5960
5961
5962
5963
5964
5965
5966
5967
5968
5969
                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!"
5970
                batch_size = len(cu_seqlens_q) - 1
5971
                if max_seqlen_q is None:
5972
5973
5974
5975
                    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]
5976
                    max_seqlen_q = int((seqlens_q.max().item() + 63) // 64 * 64)
5977
                if max_seqlen_kv is None:
5978
5979
5980
5981
                    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]
5982
                    max_seqlen_kv = int((seqlens_kv.max().item() + 63) // 64 * 64)
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
6050
6051
6052
6053
            # 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
6054
6055
6056
6057
6058
6059
            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)
6060
            context_parallel = cp_size > 1
6061
            if q_format in ["sbhd", "bshd"]:
6062
                max_seqlen_q *= cp_size
6063
                if cu_seqlens_q is None:
6064
6065
6066
6067
                    if "padding" in attn_mask_type:
                        assert (
                            attention_mask is not None
                        ), "Please provide attention_mask for padding!"
6068
                        if self.attention_type == "self":
6069
                            cu_seqlens_q = dpa_utils.get_cu_seqlens(attention_mask)
6070
                        else:
6071
                            cu_seqlens_q = dpa_utils.get_cu_seqlens(attention_mask[0])
6072
                    else:
6073
                        cu_seqlens_q = dpa_utils.get_full_cu_seqlens(
6074
6075
6076
6077
                            batch_size,
                            max_seqlen_q,
                            query_layer.device,
                        )
6078
6079
6080
6081
6082
6083
6084
6085
6086
6087
6088
6089
            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:
6090
                        cu_seqlens_kv = dpa_utils.get_full_cu_seqlens(
6091
6092
6093
6094
                            batch_size,
                            max_seqlen_kv,
                            key_layer.device,
                        )
6095

6096
            # set ALiBi attributes
6097
6098
6099
6100
6101
6102
6103
6104
            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
6105
            bottom_right_alignment = (attn_mask_type not in ["causal", "padding_causal"],)
6106
6107
6108
6109
6110
6111
6112
6113
            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
6114
                    or _alibi_cache["_bottom_right_alignment"] != bottom_right_alignment
6115
6116
6117
6118
6119
                    or _alibi_cache["_alibi_slopes"] is None
                ):
                    _alibi_cache["_alibi_slopes_require_update"] = True
                    _alibi_cache["_alibi_bias_require_update"] = True

6120
            # detect bias shape
6121
6122
            core_attention_bias_shape = None
            if core_attention_bias is not None:
6123
                if (
6124
6125
                    core_attention_bias.shape[0] == batch_size
                    and core_attention_bias.shape[1] == query_layer.shape[-2]
6126
                ):
6127
6128
6129
6130
6131
6132
6133
6134
6135
6136
6137
6138
6139
6140
6141
6142
6143
                    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"

6144
6145
6146
6147
6148
6149
6150
6151
6152
6153
6154
            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
6155

6156
            # gather attention params for get_attention_backend
6157
            attention_params = dpa_utils.AttentionParams(
6158
6159
6160
6161
                qkv_type=type(query_layer),
                qkv_dtype=query_layer.dtype,
                qkv_layout=qkv_layout,
                batch_size=batch_size,
6162
6163
                num_heads=num_attention_heads,
                num_gqa_groups=num_gqa_groups,
6164
6165
                max_seqlen_q=max_seqlen_q,
                max_seqlen_kv=max_seqlen_kv,
6166
6167
                head_dim_qk=head_dim_qk,
                head_dim_v=head_dim_v,
6168
6169
6170
6171
6172
6173
6174
6175
6176
6177
6178
                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,
6179
6180
                deterministic=self.deterministic,
                is_training=self.training,
6181
6182
                fp8=self.fp8,
                fp8_meta=self.fp8_meta,
6183
                inference_params=inference_params,
6184
            )
6185
            global _attention_backends
6186
6187
6188
6189
6190
6191
6192
6193
6194
            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,
6195
                    flash_attention_backend,
6196
6197
6198
6199
                    use_fused_attention,
                    fused_attention_backend,
                    use_unfused_attention,
                    _,
6200
6201
6202
6203
                ) = 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
6204
                _attention_backends["flash_attention_backend"] = flash_attention_backend
6205
6206
6207
6208
                _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
6209
                if use_flash_attention:
6210
6211
                    self.logger.info(
                        "Running with FlashAttention backend (version %s)",
6212
                        flash_attention_backend,
6213
                    )
6214
6215
6216
6217
                elif use_fused_attention:
                    self.logger.info(
                        "Running with FusedAttention backend (sub-backend %s)",
                        int(fused_attention_backend),
6218
                    )
6219
6220
6221
6222
                elif use_unfused_attention:
                    self.logger.info("Running with UnfusedDotProductAttention backend")
            else:
                use_flash_attention = _attention_backends["use_flash_attention"]
6223
                flash_attention_backend = _attention_backends["flash_attention_backend"]
6224
6225
6226
                use_fused_attention = _attention_backends["use_fused_attention"]
                fused_attention_backend = _attention_backends["fused_attention_backend"]
                use_unfused_attention = _attention_backends["use_unfused_attention"]
6227

6228
6229
            # raise exception if no backend is available
            if sum([use_flash_attention, use_fused_attention, use_unfused_attention]) == 0:
6230
6231
6232
6233
6234
                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."
                )
6235
6236

            # run attention
6237
6238
            if use_flash_attention:
                if core_attention_bias_type == "alibi":
6239
6240
                    alibi_slopes, _ = dpa_utils.get_alibi(
                        _alibi_cache,
6241
6242
6243
6244
6245
6246
6247
6248
6249
6250
6251
6252
6253
6254
6255
6256
6257
6258
6259
                        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,
6260
                    cp_comm_type=self.cp_comm_type,
6261
6262
                    max_seqlen_q=max_seqlen_q,
                    max_seqlen_kv=max_seqlen_kv,
6263
6264
                    fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                    fp8_meta=self.fp8_meta,
6265
                    quantizers=self.quantizers,
6266
6267
                    inference_params=inference_params,
                    flash_attention_backend=flash_attention_backend,
6268
                )
6269

6270
            if use_fused_attention:
6271
6272
                fu_core_attention_bias_type = core_attention_bias_type
                fu_core_attention_bias = core_attention_bias
6273
6274
6275
                if core_attention_bias_type == "alibi" and (
                    alibi_slopes is not None or max_seqlen_q != max_seqlen_kv
                ):
6276
                    fu_core_attention_bias_type = "post_scale_bias"
6277
6278
                    _, fu_core_attention_bias = dpa_utils.get_alibi(
                        _alibi_cache,
6279
6280
6281
6282
6283
                        query_layer.shape[-2],
                        max_seqlen_q,
                        max_seqlen_kv,
                        alibi_slopes=alibi_slopes,
                        bias_dtype=query_layer.dtype,
6284
                        bottom_right_alignment=attn_mask_type not in ["causal", "padding_causal"],
6285
                    )
6286
                # checkpoint_core_attention=False
6287
6288
6289
6290
6291
6292
6293
6294
6295
                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,
6296
6297
                        cu_seqlens_q_padded=cu_seqlens_q_padded,
                        cu_seqlens_kv_padded=cu_seqlens_kv_padded,
6298
6299
6300
6301
                        max_seqlen_q=max_seqlen_q,
                        max_seqlen_kv=max_seqlen_kv,
                        attn_mask_type=attn_mask_type,
                        attention_mask=attention_mask,
6302
                        window_size=window_size,
6303
6304
6305
6306
6307
6308
6309
                        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,
6310
                        cp_comm_type=self.cp_comm_type,
6311
6312
                        fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                        fp8_meta=self.fp8_meta,
6313
                        quantizers=self.quantizers,
6314
                        pad_between_seqs=pad_between_seqs,
6315
                        inference_params=inference_params,
6316
6317
                    )
                return self.fused_attention(
6318
6319
6320
6321
6322
6323
                    query_layer,
                    key_layer,
                    value_layer,
                    qkv_layout=qkv_layout,
                    cu_seqlens_q=cu_seqlens_q,
                    cu_seqlens_kv=cu_seqlens_kv,
6324
6325
                    cu_seqlens_q_padded=cu_seqlens_q_padded,
                    cu_seqlens_kv_padded=cu_seqlens_kv_padded,
6326
6327
                    max_seqlen_q=max_seqlen_q,
                    max_seqlen_kv=max_seqlen_kv,
6328
6329
                    attn_mask_type=attn_mask_type,
                    attention_mask=attention_mask,
6330
                    window_size=window_size,
6331
                    fused_attention_backend=fused_attention_backend,
6332
6333
                    core_attention_bias_type=fu_core_attention_bias_type,
                    core_attention_bias=fu_core_attention_bias,
6334
6335
6336
6337
                    fast_zero_fill=fast_zero_fill,
                    cp_group=self.cp_group,
                    cp_global_ranks=self.cp_global_ranks,
                    cp_stream=self.cp_stream,
6338
                    cp_comm_type=self.cp_comm_type,
6339
6340
                    fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                    fp8_meta=self.fp8_meta,
6341
                    quantizers=self.quantizers,
6342
                    pad_between_seqs=pad_between_seqs,
6343
                    inference_params=inference_params,
6344
                )
6345

6346
            from .cpu_offload import CPUOffloadEnabled
6347

6348
6349
6350
6351
6352
            if CPUOffloadEnabled:
                warnings.warn(
                    "Attention activation Offloading is only implemented"
                    "with Flash Attention and Fused Attention!"
                )
6353

6354
6355
6356
6357
6358
6359
6360
6361
6362
6363
6364
6365
            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,
6366
                        window_size=window_size,
6367
6368
6369
                        core_attention_bias_type=core_attention_bias_type,
                        core_attention_bias=core_attention_bias,
                        alibi_slopes=alibi_slopes,
6370
                        inference_params=inference_params,
6371
6372
                    )
                return self.unfused_attention(
6373
6374
6375
                    query_layer,
                    key_layer,
                    value_layer,
6376
6377
6378
6379
6380
                    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,
6381
                    window_size=window_size,
6382
6383
6384
                    core_attention_bias_type=core_attention_bias_type,
                    core_attention_bias=core_attention_bias,
                    alibi_slopes=alibi_slopes,
6385
                    inference_params=inference_params,
6386
                )
6387
            return None
6388
6389


6390
6391
6392
6393
6394
6395
6396
class MultiheadAttention(torch.nn.Module):
    r"""
    Multi-head Attention (MHA), including Query,
    Key, Value and Output projection.

    .. note::

6397
6398
        Argument :attr:`attention_mask` in the `forward` call is only used when
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
6399

6400
6401
6402
6403
6404
6405
6406
6407
6408
6409
6410
6411
6412
6413
6414
6415
6416
6417
6418
6419
6420
6421
6422
6423
6424
    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.
6425
6426
    attn_mask_type: {'no_mask', 'padding', 'causal', 'padding_causal', 'causal_bottom_right',
                   'padding_causal_bottom_right','arbitrary'},
6427
                   default = `causal`
6428
6429
6430
6431
6432
                   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.
6433
6434
6435
6436
    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
6437
6438
6439
                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
6440
                be overridden by :attr:`window_size` in `forward` as well.
6441
6442
6443
6444
6445
6446
6447
6448
6449
6450
6451
6452
6453
    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.
6454
6455
    input_layernorm: bool, default = `False`
                     if set to `True`, layer normalization to the input is applied.
6456
6457
6458
6459
6460
6461
6462
6463
6464
6465
6466
6467
6468
6469
6470
6471
6472
6473
6474
6475
    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"
6476
          The device on which the parameters of the model will be allocated. It is the user's
6477
6478
          responsibility to ensure all parameters are moved to the GPU before running the
          forward pass.
6479
6480
6481
6482
6483
6484
6485
    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.
6486
            For that, please use `get_qkv_layout` to gain the layout information.
6487
6488
    name: str, default = `None`
        name of the module, currently used for debugging purposes.
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
6523
6524
6525
6526
6527
6528

    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`.
6529
6530
6531
6532
6533
6534
    """

    def __init__(
        self,
        hidden_size: int,
        num_attention_heads: int,
6535
6536
6537
6538
6539
        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,
6540
        layer_number: Optional[int] = None,
6541
        attn_mask_type: str = "causal",
6542
        window_size: Optional[Tuple[int, int]] = None,
6543
6544
        tp_group: Optional[dist_group_type] = None,
        tp_size: int = 1,
6545
        num_gqa_groups: Optional[int] = None,
6546
6547
6548
        fuse_wgrad_accumulation: bool = False,
        get_rng_state_tracker: Optional[Callable] = None,
        sequence_parallel: bool = False,
6549
        params_dtype: Optional[torch.dtype] = None,
6550
        return_bias: bool = False,
6551
6552
6553
6554
6555
6556
6557
        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,
6558
        ub_overlap_ag: bool = False,
6559
6560
6561
6562
        ub_overlap_rs: bool = False,
        ub_overlap_rs_dgrad: bool = False,
        ub_bulk_dgrad: bool = False,
        ub_bulk_wgrad: bool = False,
6563
        bias: bool = True,
6564
        normalization: str = "LayerNorm",
6565
        device: Union[torch.device, str] = "cuda",
6566
        qkv_format: str = "sbhd",
6567
        name: str = None,
6568
6569
    ) -> None:
        super().__init__()
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        self.qkv_format = qkv_format
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        self.attn_mask_type = attn_mask_type
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        self.window_size = 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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        self.name = name

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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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                    name=name + ".layernorm_linear_qkv" if name is not None else None,
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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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                    name=name + ".linear_qkv" if name is not None else None,
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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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                    name=name + ".layernorm_linear_q" if name is not None else None,
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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,
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                    name=name + ".linear_q" if name is not None else None,
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                    **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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                name=name + ".linear_kv" if name is not None 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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            name=name + ".proj" if name is not None else None,
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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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        if TEDebugState.debug_enabled:
            TransformerEngineBaseModule._validate_name(self)

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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
6953
        # ======================
6954

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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]
6985
                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]