attention.py 313 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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from torch.utils.cpp_extension import IS_HIP_EXTENSION
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import transformer_engine_torch as tex
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from transformer_engine.pytorch.utils import (
    get_cudnn_version,
    nvtx_range_pop,
    nvtx_range_push,
)
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from transformer_engine.pytorch.cpp_extensions.fused_attn import (
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    fused_attn_fwd,
    fused_attn_bwd,
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    FusedAttnBackend,
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    META_QKV,
    META_O,
)
from transformer_engine.pytorch.fp8 import (
    FP8GlobalStateManager,
    get_fp8_te_dtype,
    get_fp8_torch_dtype,
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)
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from transformer_engine.pytorch.float8_tensor import Float8Tensor
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from transformer_engine.pytorch.tensor._internal.float8_tensor_base import Float8TensorBase
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from transformer_engine.pytorch.module import LayerNormLinear, Linear
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from transformer_engine.pytorch.module.base import TransformerEngineBaseModule
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from transformer_engine.pytorch.utils import (
    divide,
    attention_mask_func,
    split_tensor_along_dim,
    get_device_compute_capability,
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    get_default_init_method,
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)
from transformer_engine.pytorch.constants import (
    AttnMaskTypes,
    AttnTypes,
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    AttnBiasTypes,
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    QKVLayouts,
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    dist_group_type,
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    TE_DType,
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)
from transformer_engine.pytorch.softmax import FusedScaleMaskSoftmax
from transformer_engine.pytorch.distributed import (
    get_distributed_world_size,
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    get_distributed_rank,
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    checkpoint,
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    set_all_rng_states,
    CudaRNGStatesTracker,
    graph_safe_rng_available,
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    gather_along_first_dim,
    reduce_scatter_along_first_dim,
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)
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from transformer_engine.pytorch.jit import jit_fuser, no_torch_dynamo
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from transformer_engine.pytorch.graph import is_graph_capturing
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from transformer_engine.pytorch.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.inference import InferenceParams
from transformer_engine.pytorch.dot_product_attention.utils import FlashAttentionUtils as fa_utils
from transformer_engine.pytorch.dot_product_attention.utils import AttentionLogging as attn_log
from transformer_engine.pytorch.dot_product_attention.rope import apply_rotary_pos_emb
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# Setup Attention Logging
attn_log.setup_logging()

# Global vars for flash attn 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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    if (
        torch.cuda.is_available()
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        and (IS_HIP_EXTENSION or get_device_compute_capability() >= (8, 0))
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        and dpa_utils._NVTE_FLASH_ATTN
    ):
        attn_log.fa_logger.debug(
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            "flash-attn v2 is not installed. To use, please install it by"
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            """ "pip3 install flash-attn".""",
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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()
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        and (IS_HIP_EXTENSION or get_device_compute_capability() >= (8, 0))
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        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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        )

# Detect flash-attn v3 in the environment
# This section will be removed when FA3 is released as a regular FA package,
# i.e. flashattn-hopper 3.0.0 as flash-attn 3.0.0
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if not IS_HIP_EXTENSION:
    try:
        fa_utils.fa3_version = PkgVersion(get_pkg_version("flashattn-hopper"))
    except PackageNotFoundError:
        if (
            torch.cuda.is_available()
            and get_device_compute_capability() >= (9, 0)
            and dpa_utils._NVTE_FLASH_ATTN
        ):
            attn_log.fa_logger.debug(
                "flash-attn v3 is not installed. To use, please install it by \n%s",
                fa_utils.v3_installation_steps,
            )
    else:
        from flashattn_hopper.flash_attn_interface import flash_attn_func as flash_attn_func_v3
        from flashattn_hopper.flash_attn_interface import (
            flash_attn_varlen_func as flash_attn_varlen_func_v3,
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        )
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        from flashattn_hopper.flash_attn_interface import _flash_attn_forward as _flash_attn_fwd_v3
        from flashattn_hopper.flash_attn_interface import _flash_attn_backward as _flash_attn_bwd_v3
        from flashattn_hopper.flash_attn_interface import (
            _flash_attn_varlen_forward as _flash_attn_varlen_fwd_v3,
        )
        from flashattn_hopper.flash_attn_interface import (
            _flash_attn_varlen_backward as _flash_attn_varlen_bwd_v3,
        )
    
        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,
    "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)]


492
class AttnFuncWithCPAndKVP2P(torch.autograd.Function):
493
    """
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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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    ):
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        # pylint: disable=missing-function-docstring
534
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndKVP2P.forward")
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        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)

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

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

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

        (
            QKV_quantizer,
            O_quantizer,
            O_CP_quantizer,
            S_quantizer,
            dQKV_quantizer,
            dQKV_CP_quantizer,
            dO_quantizer,
            dP_quantizer,
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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()
                    S_quantizer_per_step[i].amax = amax_per_step[0][i]
                    O_CP_quantizer_per_step[i] = O_CP_quantizer.copy()
                    O_CP_quantizer_per_step[i].amax = amax_per_step[1][i]
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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")):
650
                q_f16 = q
651
                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]]
663
        if attn_bias is not None:
664
            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!"
671
            # [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)
682
            )
683
        assert q.shape[-1] % 8 == 0, "hidden size per attention head should be multiple of 8"
684

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        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:
690
                softmax_lse_in_packed_format = fa_utils.v2_6_0_plus or fa_utils.use_v3
691

692
        flash_attn_fwd = None
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        if not use_fused_attention:
            fa_forward_kwargs = {"softmax_scale": softmax_scale}
695
            if fa_utils.use_v3:
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                if qkv_format == "thd":
                    flash_attn_fwd = _flash_attn_varlen_fwd_v3
                else:
                    flash_attn_fwd = _flash_attn_fwd_v3
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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
708
                if (fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus) or fa_utils.use_v3:
709
                    fa_forward_kwargs["window_size"] = (-1, 0) if causal else (-1, -1)
710
                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
713
                if fa_utils.v2_4_plus:
714
                    fa_forward_kwargs["alibi_slopes"] = None
715
                if fa_utils.v2_5_7_plus and qkv_format == "thd":
716
                    fa_forward_kwargs["block_table"] = None
717
                if fa_utils.v2_6_0_plus:
718
                    fa_forward_kwargs["softcap"] = 0.0
719

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        # Flash Attn inputs
        q_inputs = [None, None]
        kv_inputs = [None, None]
723
        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)]
728
        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)]
736
        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 = [[], []]

742
        out = None
743
        for i in range(cp_size + 1):
744
            if i < cp_size:
745
                with torch.cuda.stream(flash_attn_streams[i % 2]):
746
                    # wait until KV is received
747
                    for req in send_recv_reqs[(i + 1) % 2]:
748
749
                        req.wait()

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                    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,
                        )

762
                    if not fp8 or is_input_fp8 or int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
763
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765
                        kv_inputs[i % 2] = p2p_comm_buffers[i]
                    else:
                        # KV exchange is in BF16/FP16, cast received KV in each step
766
                        kv_inputs[i % 2] = QKV_quantizer(p2p_comm_buffers[i])._data
767
768
                    if causal:
                        if i == 0:
769
                            if pad_between_seqs:
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775
                                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
                                )
776
777
                            elif qkv_format == "thd":
                                cu_seqlens_q_per_step[i] = cu_seqlens_q // cp_size
778
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
779
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781
                            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
798
                            if use_fused_attention:
799
800
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
801
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806
                                    attn_bias_inputs[i % 2] = torch.cat(
                                        (
                                            attn_bias[..., idx, :],
                                            attn_bias[..., (2 * cp_size - idx - 1), :],
                                        ),
                                        dim=-1,
807
                                    ).contiguous()
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819

                                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]
                                )
820
                                fp8_meta_kwargs = {}
821
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830
                                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
                                    )
831
832
                                    fp8_meta_kwargs["s_quantizer"] = S_quantizer_per_step[i]
                                    fp8_meta_kwargs["o_quantizer"] = O_CP_quantizer_per_step[i]
833

834
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837
838
839
                                out_per_step[i], aux_ctx_tensors = fused_attn_fwd(
                                    is_training,
                                    max_seqlen_q,
                                    max_seqlen_kv,
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
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844
                                    q_part,
                                    k_part,
                                    v_part,
                                    fake_dtype=qkv_dtype,
                                    fused_attention_backend=fused_attn_backend,
845
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853
                                    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,
854
                                )
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                                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
860
                            else:
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867
868
                                fa_forward_args_thd = []
                                if qkv_format == "thd":
                                    fa_forward_args_thd = [
                                        cu_seqlens_q_per_step[i],
                                        cu_seqlens_kv_per_step[i],
                                        max_seqlen_q,
                                        max_seqlen_kv,
                                    ]
869
                                fa_outputs = flash_attn_fwd(
870
                                    q_inputs[i % 2],
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881
                                    (
                                        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,
882
                                    causal=True,
883
                                    **fa_forward_kwargs,
884
                                )
885
                                if not fa_utils.v2_7_0_plus:
886
887
                                    out_per_step[i] = fa_outputs[4]
                                    softmax_lse_per_step[i] = fa_outputs[5]
888
                                    if not fa_utils.use_v3:
889
890
891
892
                                        rng_states[i] = fa_outputs[7]
                                else:
                                    out_per_step[i] = fa_outputs[0]
                                    softmax_lse_per_step[i] = fa_outputs[1]
893
                                    if not fa_utils.use_v3:
894
                                        rng_states[i] = fa_outputs[3]
895
                        elif i <= rank:
896
                            if pad_between_seqs:
897
898
899
900
901
902
903
904
905
906
907
                                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,
                                )
908
909
                            elif qkv_format == "thd":
                                cu_seqlens_q_per_step[i] = cu_seqlens_q // cp_size
910
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // (cp_size * 2)
911
912
913
                            else:
                                cu_seqlens_q_per_step[i] = cu_seqlens_q
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv_half
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
                            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
                                )
930
                            if use_fused_attention:
931
                                kv_inputs[i % 2] = kv_inputs[i % 2].contiguous()
932
933
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
934
                                    attn_bias_inputs[i % 2] = attn_bias[..., idx, :].contiguous()
935
936
937
938
939
940
941
942
943
944
945
946

                                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]
                                )
947
                                fp8_meta_kwargs = {}
948
949
950
951
952
953
954
955
956
957
                                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
                                    )
958
959
                                    fp8_meta_kwargs["s_quantizer"] = S_quantizer_per_step[i]
                                    fp8_meta_kwargs["o_quantizer"] = O_CP_quantizer_per_step[i]
960
961
962
963
964
965
                                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],
966
967
968
969
                                    q_part,
                                    k_part,
                                    v_part,
                                    qkv_dtype,
970
971
972
973
974
975
976
977
978
979
980
981
982
983
                                    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,
984
                                )
985
986
987
988
989
                                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
990
                            else:
991
                                fa_forward_args_thd = []
992
                                if qkv_format == "thd":
993
994
995
996
997
998
                                    fa_forward_args_thd = [
                                        cu_seqlens_q_per_step[i],
                                        cu_seqlens_kv_per_step[i],
                                        max_seqlen_q,
                                        max_seqlen_kv // 2,
                                    ]
999
1000
                                if fa_utils.use_v3 or (
                                    fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus
1001
                                ):
1002
                                    fa_forward_kwargs["window_size"] = (-1, -1)
1003
                                elif fa_utils.v2_7_0_plus:
1004
1005
                                    fa_forward_kwargs["window_size_left"] = -1
                                    fa_forward_kwargs["window_size_right"] = -1
1006
                                fa_outputs = flash_attn_fwd(
1007
                                    q_inputs[i % 2],
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
                                    (
                                        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,
1019
                                    causal=False,
1020
                                    **fa_forward_kwargs,
1021
                                )
1022
                                if not fa_utils.v2_7_0_plus:
1023
1024
                                    out_per_step[i] = fa_outputs[4]
                                    softmax_lse_per_step[i] = fa_outputs[5]
1025
                                    if not fa_utils.use_v3:
1026
1027
1028
1029
                                        rng_states[i] = fa_outputs[7]
                                else:
                                    out_per_step[i] = fa_outputs[0]
                                    softmax_lse_per_step[i] = fa_outputs[1]
1030
                                    if not fa_utils.use_v3:
1031
                                        rng_states[i] = fa_outputs[3]
1032
                        else:
1033
                            if pad_between_seqs:
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
                                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,
                                )
1045
1046
                            elif qkv_format == "thd":
                                cu_seqlens_q_per_step[i] = cu_seqlens_q // (cp_size * 2)
1047
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
1048
1049
1050
                            else:
                                cu_seqlens_q_per_step[i] = cu_seqlens_q_half
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
                            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
                                )
1070
                            if use_fused_attention:
1071
                                q_inputs[i % 2] = q_inputs[i % 2].contiguous()
1072
1073
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
1074
1075
1076
1077
1078
1079
                                    attn_bias_inputs[i % 2] = torch.cat(
                                        (
                                            attn_bias_[..., 1, :, idx, :],
                                            attn_bias_[..., 1, :, (2 * cp_size - idx - 1), :],
                                        ),
                                        dim=-1,
1080
                                    ).contiguous()
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092

                                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]
                                )
1093
                                fp8_meta_kwargs = {}
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
                                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
                                    )
1104
1105
                                    fp8_meta_kwargs["s_quantizer"] = S_quantizer_per_step[i]
                                    fp8_meta_kwargs["o_quantizer"] = O_CP_quantizer_per_step[i]
1106
1107
1108
1109
1110
1111
                                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],
1112
1113
1114
1115
                                    q_part,
                                    k_part,
                                    v_part,
                                    qkv_dtype,
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
                                    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,
1130
                                )
1131
1132
1133
1134
1135
                                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
1136
                            else:
1137
                                fa_forward_args_thd = []
1138
                                if qkv_format == "thd":
1139
1140
1141
1142
1143
1144
                                    fa_forward_args_thd = [
                                        cu_seqlens_q_per_step[i],
                                        cu_seqlens_kv_per_step[i],
                                        max_seqlen_q // 2,
                                        max_seqlen_kv,
                                    ]
1145
1146
                                if fa_utils.use_v3 or (
                                    fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus
1147
                                ):
1148
                                    fa_forward_kwargs["window_size"] = (-1, -1)
1149
                                elif fa_utils.v2_7_0_plus:
1150
1151
                                    fa_forward_kwargs["window_size_left"] = -1
                                    fa_forward_kwargs["window_size_right"] = -1
1152
                                fa_outputs = flash_attn_fwd(
1153
                                    q_inputs[i % 2],
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
                                    (
                                        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,
1165
                                    causal=False,
1166
                                    **fa_forward_kwargs,
1167
                                )
1168
                                if not fa_utils.v2_7_0_plus:
1169
1170
                                    out_per_step[i] = fa_outputs[4]
                                    softmax_lse_per_step[i] = fa_outputs[5]
1171
                                    if not fa_utils.use_v3:
1172
1173
1174
1175
                                        rng_states[i] = fa_outputs[7]
                                else:
                                    out_per_step[i] = fa_outputs[0]
                                    softmax_lse_per_step[i] = fa_outputs[1]
1176
                                    if not fa_utils.use_v3:
1177
                                        rng_states[i] = fa_outputs[3]
1178
                    else:
1179
                        if pad_between_seqs:
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
                            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,
                            )
1191
1192
                        elif qkv_format == "thd":
                            cu_seqlens_q_per_step[i] = cu_seqlens_q // cp_size
1193
                            cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
1194
1195
1196
                        else:
                            cu_seqlens_q_per_step[i] = cu_seqlens_q
                            cu_seqlens_kv_per_step[i] = cu_seqlens_kv
1197
                        if use_fused_attention:
1198
1199
                            if attn_bias is not None:
                                idx = (rank - i) % cp_size
1200
1201
1202
1203
1204
1205
                                attn_bias_inputs[i % 2] = torch.cat(
                                    (
                                        attn_bias[..., idx, :],
                                        attn_bias[..., (2 * cp_size - idx - 1), :],
                                    ),
                                    dim=-1,
1206
                                ).contiguous()
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218

                            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]
                            )
1219
                            fp8_meta_kwargs = {}
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
                            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
                                )
1230
1231
                                fp8_meta_kwargs["s_quantizer"] = S_quantizer_per_step[i]
                                fp8_meta_kwargs["o_quantizer"] = O_CP_quantizer_per_step[i]
1232
1233
1234
1235
1236
1237
                            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],
1238
1239
1240
1241
                                q_part,
                                k_part,
                                v_part,
                                qkv_dtype,
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
                                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,
1252
                            )
1253
1254
1255
1256
1257
                            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
1258
                        else:
1259
1260
1261
1262
1263
1264
1265
1266
                            fa_forward_args_thd = []
                            if qkv_format == "thd":
                                fa_forward_args_thd = [
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
                                    max_seqlen_q,
                                    max_seqlen_kv,
                                ]
1267
                            fa_outputs = flash_attn_fwd(
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
                                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,
1280
                                causal=False,
1281
                                **fa_forward_kwargs,
1282
                            )
1283
                            if not fa_utils.v2_7_0_plus:
1284
1285
                                out_per_step[i] = fa_outputs[4]
                                softmax_lse_per_step[i] = fa_outputs[5]
1286
                                if not fa_utils.use_v3:
1287
1288
1289
1290
                                    rng_states[i] = fa_outputs[7]
                            else:
                                out_per_step[i] = fa_outputs[0]
                                softmax_lse_per_step[i] = fa_outputs[1]
1291
                                if not fa_utils.use_v3:
1292
                                    rng_states[i] = fa_outputs[3]
1293
1294
1295
1296

            if i > 0:
                # wait until fwd restuls correction of last step is done
                if i > 1:
1297
                    flash_attn_streams[(i - 1) % 2].wait_event(fwd_results_correction_done)
1298

1299
                if use_fused_attention:
1300
1301
                    # [b, np, sq, 1] -> [b, np, sq] or
                    # [t, np, 1] -> [t, np]
1302
                    softmax_lse_per_step[i - 1].squeeze_(-1)
1303
1304
1305
1306
                    if softmax_lse_in_packed_format:
                        softmax_lse_per_step[i - 1] = (
                            softmax_lse_per_step[i - 1].transpose(0, 1).contiguous()
                        )
1307

1308
                with torch.cuda.stream(flash_attn_streams[(i - 1) % 2]):
1309
                    if fp8:
1310
                        out_per_step[i - 1] = out_per_step[i - 1].dequantize(dtype=torch.float32)
1311
1312
                    if i == 1:
                        softmax_lse = torch.clone(softmax_lse_per_step[0]).to(torch.double)
1313
1314
                        if qkv_format == "thd":
                            out = torch.zeros_like(q if not fp8 else out_per_step[0]).view(q.shape)
1315
1316
1317
1318
                    elif (i - 1) <= rank or not causal:
                        flash_attn_fwd_softmax_lse_correction(
                            softmax_lse, softmax_lse_per_step[i - 1]
                        )
1319
                    else:
1320
                        if qkv_format == "thd":
1321
                            tex.thd_second_half_lse_correction(
1322
1323
1324
                                softmax_lse,
                                softmax_lse_per_step[i - 1],
                                cu_seqlens_q_padded,
1325
                                softmax_lse_in_packed_format,
1326
                            )
1327
                        else:
1328
1329
1330
                            flash_attn_fwd_second_half_softmax_lse_correction(
                                softmax_lse.view(*softmax_lse.shape[:-1], 2, -1),
                                softmax_lse_per_step[i - 1],
1331
                            )
1332
1333

                if i < cp_size:
1334
                    flash_attn_streams[(i - 1) % 2].record_event(fwd_results_correction_done)
1335
1336
1337

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

1338
1339
1340
1341
        second_half_lse_seqlen = None
        if causal and rank < (cp_size - 1):
            second_half_lse_seqlen = softmax_lse_per_step[-1].shape[-1]

1342
1343
        softmax_lse = softmax_lse.to(torch.float)
        for i in range(cp_size):
1344
            if i <= rank or not causal:
1345
                if qkv_format in ["bshd", "sbhd"]:
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
                    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,
                        )
1362
                elif qkv_format == "thd":
1363
1364
1365
1366
1367
                    tex.thd_out_correction(
                        out,
                        out_per_step[i],
                        softmax_lse,
                        softmax_lse_per_step[i],
1368
                        cu_seqlens_q_padded,
1369
                        False,
1370
                        softmax_lse_in_packed_format,
1371
                    )
1372
            else:
1373
                if qkv_format in ["bshd", "sbhd"]:
1374
1375
                    flash_attn_fwd_second_half_out_correction(
                        out,
1376
                        out_per_step[i],
1377
                        softmax_lse,
1378
                        softmax_lse_per_step[i],
1379
                        seq_dim,
1380
                    )
1381
                elif qkv_format == "thd":
1382
1383
1384
1385
1386
                    tex.thd_out_correction(
                        out,
                        out_per_step[i],
                        softmax_lse,
                        softmax_lse_per_step[i],
1387
                        cu_seqlens_q_padded,
1388
                        True,
1389
                        softmax_lse_in_packed_format,
1390
                    )
1391
1392

        kv = p2p_comm_buffers[-1]
1393
1394
1395
1396
1397
1398
1399
1400
        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:
1401
            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_after_attn(cp_size_a2a, out.device)
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
            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:
1413
            out = out.view(-1, *out.shape[-2:])
1414

1415
1416
1417
1418
1419
        if fp8 and use_fused_attention:
            amax_cp_fwd = amax_per_step.amax(dim=1)
            S_quantizer.amax = amax_cp_fwd[0]
            O_CP_quantizer.amax = amax_cp_fwd[1]

1420
        out_fp8 = None
1421
        out_f16 = out.to(qkv_dtype)
1422

1423
        if fp8 and (is_output_fp8 or int(os.getenv("NVTE_FP8_DPA_BWD", "1"))):
1424
1425
1426
            out_fp8 = O_quantizer(out_f16)  # final result

        out_ret = out_fp8 if (fp8 and is_output_fp8) else out_f16
1427
1428

        if fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
1429
            q_save, kv_save, out_save = q, kv, out_fp8._data
1430
        elif fp8 and is_input_fp8:
1431
            q_save, kv_save, out_save = q, kv, out_f16
1432
        else:
1433
            q_f16 = q_f16.view(q.shape)
1434
1435
            q_save, kv_save, out_save = q_f16, kv, out_f16

1436
        tensors_to_save, tensor_objects = prepare_for_saving(
1437
1438
1439
            q_save,
            kv_save,
            out_save,
1440
            softmax_lse,
1441
1442
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
1443
1444
            *cu_seqlens_q_per_step,
            *cu_seqlens_kv_per_step,
1445
1446
            *rng_states,
            *attn_biases,
1447
        )
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
        ctx.save_for_backward(*tensors_to_save)
        ctx.tensor_objects = tensor_objects

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

1461
1462
1463
        ctx.cp_group_a2a = cp_group_a2a
        ctx.cp_size_a2a = cp_size_a2a
        ctx.rank_a2a = rank_a2a
1464
1465
        ctx.cp_group = cp_group
        ctx.cp_global_ranks = cp_global_ranks
1466
        ctx.cp_stream = cp_stream
1467
1468
        ctx.dropout_p = dropout_p
        ctx.max_seqlen_q = max_seqlen_q
1469
        ctx.max_seqlen_kv = max_seqlen_kv
1470
        ctx.softmax_scale = softmax_scale
1471
        ctx.qkv_format = qkv_format
1472
        ctx.attn_mask_type = attn_mask_type
1473
1474
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_bias_shape = None if attn_bias is None else attn_bias.shape
1475
        ctx.deterministic = deterministic
1476
        ctx.use_fused_attention = use_fused_attention
1477
        ctx.softmax_lse_in_packed_format = softmax_lse_in_packed_format
1478
        ctx.second_half_lse_seqlen = second_half_lse_seqlen
1479
1480
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        ctx.fp8_meta = fp8_meta
1481
1482
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
1483
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndKVP2P.forward")
1484

1485
        return out_ret
1486
1487
1488

    @staticmethod
    def backward(ctx, dout):
1489
        # pylint: disable=missing-function-docstring
1490
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndKVP2P.backward")
1491
1492
1493
        cp_size_a2a = ctx.cp_size_a2a
        rank_a2a = ctx.rank_a2a

1494
1495
        cp_size = get_distributed_world_size(ctx.cp_group)
        rank = get_distributed_rank(ctx.cp_group)
1496
1497
        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]
1498
1499
        batch_p2p_comm = int(os.getenv("NVTE_BATCH_MHA_P2P_COMM", "0")) or (cp_size == 2)

1500
        q, kv, out, softmax_lse, cu_seqlens_q_padded, cu_seqlens_kv_padded, *other_tensors = (
1501
            restore_from_saved(ctx.tensor_objects, ctx.saved_tensors)
1502
1503
1504
1505
1506
        )
        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]
1507

1508
1509
        causal = "causal" in ctx.attn_mask_type
        padding = "padding" in ctx.attn_mask_type
1510
1511

        seq_dim = None
1512
        if ctx.qkv_format in ["bshd", "sbhd"]:
1513
            seq_dim = ctx.qkv_format.index("s")
1514
1515
1516
            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
1517

1518
        if attn_biases[0] is not None:
1519
1520
            # [b, np, sq, 2*cp, sk//(2*cp)]
            attn_dbias = torch.zeros(
1521
                *ctx.attn_bias_shape, dtype=attn_biases[0].dtype, device=attn_biases[0].device
1522
1523
1524
            )
            # [b, np, sq, 2*cp, sk//(2*cp)] -> [b, np, 2, sq//2, 2*cp, sk//(2*cp)]
            attn_dbias_ = attn_dbias.view(
1525
                *attn_dbias.shape[:-3], 2, attn_dbias.shape[-3] // 2, *attn_dbias.shape[-2:]
1526
1527
1528
            )
        else:
            attn_dbias = None
1529
            attn_dbias_ = None
1530

1531
1532
        softmax_lse_ = None
        if causal and ctx.second_half_lse_seqlen is not None:
1533
            if ctx.qkv_format == "thd":
1534
                softmax_lse_ = tex.thd_read_second_half_lse(
1535
1536
1537
1538
                    softmax_lse,
                    cu_seqlens_q_padded,
                    ctx.softmax_lse_in_packed_format,
                    ctx.second_half_lse_seqlen,
1539
                )
1540
1541
            else:
                # [b, np, sq] -> [b, np, 2, sq//2]
1542
                softmax_lse_ = softmax_lse.view(*softmax_lse.shape[:-1], 2, -1)
1543
                softmax_lse_ = softmax_lse_[..., 1, :].contiguous()
1544
1545
1546
1547
1548
1549
            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)
1550
        if ctx.use_fused_attention:
1551
1552
1553
1554
            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]
1555
            softmax_lse.unsqueeze_(-1)
1556
            dout = dout.contiguous()
1557

1558
        dq = None
1559
        dout_dtype = dout.dtype
1560
1561
        fused_attn_backend = None
        fused_attn_dqkv_dtype = None
1562
1563
1564
        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)]
1565
1566
1567
        if ctx.fp8:
            if ctx.use_fused_attention:
                fused_attn_backend = FusedAttnBackend["FP8"]
1568

1569
1570
1571
1572
1573
1574
1575
1576
1577
                dqkv_fp8_torch_dtype = get_fp8_torch_dtype(
                    ctx.fp8_meta["recipe"], fprop_tensor=False
                )
                dq_fp8 = torch.empty(
                    (cp_size, *q.shape), dtype=dqkv_fp8_torch_dtype, device=q.device
                )
                dkv_fp8 = torch.empty(
                    (cp_size, *kv.shape), dtype=dqkv_fp8_torch_dtype, device=kv.device
                )
1578
                dkv_fp8_ = torch.empty_like(dkv_fp8)
1579
                if ctx.is_output_fp8:
1580
                    assert isinstance(dout, Float8Tensor), "dout must be Float8Tensors for FP8 MHA!"
1581
                    ctx.dO_quantizer = dout._quantizer
1582
                else:
1583
                    dout = ctx.dO_quantizer(dout)
1584
1585
                fused_attn_dqkv_dtype = dout._fp8_dtype
                dout = dout._data
1586
1587
                p2p_comm_buffers = [[kv, dkv_fp8], [torch.empty_like(kv), dkv_fp8_]]
                fp8_meta_kwargs = {}
1588
                fp8_meta_kwargs["s_quantizer"] = ctx.S_quantizer
1589
1590
1591
1592
1593
1594
                amax_per_step = torch.zeros((2, cp_size), dtype=torch.float32, device=q.device)
                for i in range(cp_size):
                    dP_quantizer_per_step[i] = ctx.dP_quantizer.copy()
                    dP_quantizer_per_step[i].amax = amax_per_step[0][i]
                    dQKV_CP_quantizer_per_step[i] = ctx.dQKV_CP_quantizer.copy()
                    dQKV_CP_quantizer_per_step[i].amax = amax_per_step[1][i]
1595
1596
1597
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
            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
1615
1616
1617
1618
1619
1620
1621
1622
            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 = {}
1623
                fused_attn_dqkv_dtype = TE_DType[dout_dtype]
1624
1625
                fused_attn_backend = FusedAttnBackend["F16_arbitrary_seqlen"]

1626
1627
1628
1629
        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)
1630
1631
1632
            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_before_attn(
                cp_size_a2a, out.device
            )
1633
1634
1635
1636
1637
1638
1639
1640
1641
            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,
            )
1642
            if not ctx.fp8 and ctx.fp8_meta is not None and ctx.is_output_fp8:
1643
1644
1645
1646
                dout = ctx.dO_quantizer.create_tensor_from_data(
                    dout, fake_dtype=dout_dtype, internal=True
                )
                dout = dout.dequantize(dtype=dout_dtype)
1647

1648
1649
1650
1651
        out = out.view(*q.shape)
        dout = dout.view(*q.shape)
        send_recv_reqs = []

1652
        flash_attn_bwd = None
1653
1654
        if not ctx.use_fused_attention:
            fa_backward_kwargs = {"softmax_scale": ctx.softmax_scale}
1655
            if fa_utils.use_v3:
1656
1657
1658
1659
                if ctx.qkv_format == "thd":
                    flash_attn_bwd = _flash_attn_varlen_bwd_v3
                else:
                    flash_attn_bwd = _flash_attn_bwd_v3
1660
1661
                fa_backward_kwargs["deterministic"] = ctx.deterministic
            else:
1662
1663
1664
1665
                if ctx.qkv_format == "thd":
                    flash_attn_bwd = _flash_attn_varlen_bwd
                else:
                    flash_attn_bwd = _flash_attn_bwd
1666
                fa_backward_kwargs["dropout_p"] = ctx.dropout_p
1667
                if fa_utils.v2_4_plus:
1668
                    fa_backward_kwargs["alibi_slopes"] = None
1669
                if fa_utils.v2_4_1_plus:
1670
                    fa_backward_kwargs["deterministic"] = ctx.deterministic
1671
                if fa_utils.v2_6_0_plus:
1672
                    fa_backward_kwargs["softcap"] = 0.0
1673

1674
1675
1676
1677
1678
        for i in range(cp_size):
            # wait until KV is received
            for req in send_recv_reqs:
                req.wait()

1679
1680
            send_tensor = p2p_comm_buffers[i % 2]
            recv_tensor = p2p_comm_buffers[(i + 1) % 2]
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
            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
                )
1710

1711
            kv = p2p_comm_buffers[i % 2][0]
1712
1713
            q_, kv_, out_, dout_ = None, None, None, None
            dq_, dk_, dv_ = None, None, None
1714
            # In reversed order of fwd
1715
            if causal:
1716
                if i == (cp_size - 1):
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
                    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
1731
                    if ctx.use_fused_attention:
1732
1733
1734
1735
1736
1737
1738
1739
                        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]]
1740
                        if attn_dbias is not None:
1741
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
                        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(
1762
                                dout_part, fake_dtype=dout_dtype, internal=True
1763
                            )
1764
1765
                            fp8_meta_kwargs["dp_quantizer"] = dP_quantizer_per_step[i]
                            fp8_meta_kwargs["dqkv_quantizer"] = dQKV_CP_quantizer_per_step[i]
1766
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
1767
                            ctx.max_seqlen_q,
1768
1769
1770
                            ctx.max_seqlen_kv,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
1771
1772
1773
1774
1775
1776
                            q_part,
                            k_part,
                            v_part,
                            out_part,
                            dout_part,
                            ctx.qkv_dtype,
1777
                            fused_attn_dqkv_dtype,
1778
                            aux_ctx_tensors,
1779
                            fused_attn_backend,
1780
1781
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
                            cu_seqlens_kv_padded=cu_seqlens_kv_padded,
1782
1783
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
1784
                            qkv_layout=qkv_layout,
1785
                            attn_mask_type=ctx.attn_mask_type,
1786
                            attn_bias_type=ctx.attn_bias_type,
1787
1788
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
1789
                        )
1790
1791
1792
1793
                        if ctx.fp8:
                            dq_ = dq_._data
                            dk_ = dk_._data
                            dv_ = dv_._data
1794
                    else:
1795
                        dq_ = torch.empty_like(q_)
1796
                        dkv_ = torch.empty_like(kv_)
1797
1798
1799
1800
1801
1802
1803
1804
                        fa_backward_args_thd = []
                        if ctx.qkv_format == "thd":
                            fa_backward_args_thd = [
                                cu_seqlens_q_per_step[cp_size - i - 1],
                                cu_seqlens_kv_per_step[cp_size - i - 1],
                                ctx.max_seqlen_q,
                                ctx.max_seqlen_kv,
                            ]
1805
                        if fa_utils.use_v3 or (fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus):
1806
                            fa_backward_kwargs["window_size"] = (-1, 0)
1807
                        elif fa_utils.v2_7_0_plus:
1808
1809
                            fa_backward_kwargs["window_size_left"] = -1
                            fa_backward_kwargs["window_size_right"] = 0
1810
                        if not fa_utils.use_v3:
1811
1812
                            fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                        flash_attn_bwd(
1813
1814
                            dout_,
                            q_,
1815
1816
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
1817
1818
1819
                            out_,
                            softmax_lse,
                            dq_,
1820
1821
1822
                            dkv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else dkv_[0],
                            dkv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else dkv_[1],
                            *fa_backward_args_thd,
1823
1824
                            causal=True,
                            **fa_backward_kwargs,
1825
                        )
1826
                elif i >= (cp_size - rank - 1):
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
                    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)
1843
                    if ctx.use_fused_attention:
1844
                        kv_ = kv_.contiguous()
1845
1846
1847
1848
1849
1850
1851
1852
                        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]]
1853
                        if attn_dbias is not None:
1854
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
                        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(
1875
                                dout_part, fake_dtype=dout_dtype, internal=True
1876
                            )
1877
1878
                            fp8_meta_kwargs["dp_quantizer"] = dP_quantizer_per_step[i]
                            fp8_meta_kwargs["dqkv_quantizer"] = dQKV_CP_quantizer_per_step[i]
1879
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
1880
                            ctx.max_seqlen_q,
1881
1882
1883
                            ctx.max_seqlen_kv // 2,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
1884
1885
1886
1887
1888
1889
                            q_part,
                            k_part,
                            v_part,
                            out_part,
                            dout_part,
                            ctx.qkv_dtype,
1890
                            fused_attn_dqkv_dtype,
1891
                            aux_ctx_tensors,
1892
                            fused_attn_backend,
1893
1894
1895
1896
                            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
                            ),
1897
1898
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
1899
                            qkv_layout=qkv_layout,
1900
                            attn_mask_type="padding" if padding else "no_mask",
1901
                            attn_bias_type=ctx.attn_bias_type,
1902
1903
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
1904
                        )
1905
1906
1907
1908
                        if ctx.fp8:
                            dq_ = dq_._data
                            dk_ = dk_._data
                            dv_ = dv_._data
1909
                    else:
1910
                        dq_ = torch.empty_like(q_)
1911
                        dkv_ = torch.empty_like(kv_)
1912
1913
1914
1915
1916
1917
1918
1919
                        fa_backward_args_thd = []
                        if ctx.qkv_format == "thd":
                            fa_backward_args_thd = [
                                cu_seqlens_q_per_step[cp_size - i - 1],
                                cu_seqlens_kv_per_step[cp_size - i - 1],
                                ctx.max_seqlen_q,
                                ctx.max_seqlen_kv // 2,
                            ]
1920
                        if fa_utils.use_v3 or (fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus):
1921
                            fa_backward_kwargs["window_size"] = (-1, -1)
1922
                        if fa_utils.v2_7_0_plus:
1923
1924
                            fa_backward_kwargs["window_size_left"] = -1
                            fa_backward_kwargs["window_size_right"] = -1
1925
                        if not fa_utils.use_v3:
1926
1927
                            fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                        flash_attn_bwd(
1928
1929
                            dout_,
                            q_,
1930
1931
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
1932
1933
1934
                            out_,
                            softmax_lse,
                            dq_,
1935
1936
1937
                            dkv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else dkv_[0],
                            dkv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else dkv_[1],
                            *fa_backward_args_thd,
1938
1939
                            causal=False,
                            **fa_backward_kwargs,
1940
1941
                        )
                else:
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
                    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
1959
                    if ctx.use_fused_attention:
1960
                        q_, out_, dout_ = [x.contiguous() for x in [q_, out_, dout_]]
1961
1962
1963
1964
1965
1966
1967
1968
                        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]]
1969
                        if attn_dbias is not None:
1970
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991

                        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(
1992
                                dout_part, fake_dtype=dout_dtype, internal=True
1993
                            )
1994
1995
                            fp8_meta_kwargs["dp_quantizer"] = dP_quantizer_per_step[i]
                            fp8_meta_kwargs["dqkv_quantizer"] = dQKV_CP_quantizer_per_step[i]
1996
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
1997
                            ctx.max_seqlen_q // 2,
1998
1999
2000
                            ctx.max_seqlen_kv,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2001
2002
2003
2004
2005
2006
                            q_part,
                            k_part,
                            v_part,
                            out_part,
                            dout_part,
                            ctx.qkv_dtype,
2007
                            fused_attn_dqkv_dtype,
2008
                            aux_ctx_tensors,
2009
                            fused_attn_backend,
2010
2011
2012
2013
                            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,
2014
2015
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
2016
                            qkv_layout=qkv_layout,
2017
                            attn_mask_type="padding" if padding else "no_mask",
2018
                            attn_bias_type=ctx.attn_bias_type,
2019
2020
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
2021
                        )
2022
2023
2024
2025
                        if ctx.fp8:
                            dq_ = dq_._data
                            dk_ = dk_._data
                            dv_ = dv_._data
2026
                    else:
2027
                        dq_ = torch.empty_like(q_)
2028
                        dkv_ = torch.empty_like(kv_)
2029
                        fa_backward_args_thd = []
2030
                        if ctx.qkv_format == "thd":
2031
2032
2033
2034
2035
2036
                            fa_backward_args_thd = [
                                cu_seqlens_q_per_step[cp_size - i - 1],
                                cu_seqlens_kv_per_step[cp_size - i - 1],
                                ctx.max_seqlen_q // 2,
                                ctx.max_seqlen_kv,
                            ]
2037
                        if fa_utils.use_v3 or (fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus):
2038
                            fa_backward_kwargs["window_size"] = (-1, -1)
2039
                        elif fa_utils.v2_7_0_plus:
2040
2041
                            fa_backward_kwargs["window_size_left"] = -1
                            fa_backward_kwargs["window_size_right"] = -1
2042
                        if not fa_utils.use_v3:
2043
2044
                            fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                        flash_attn_bwd(
2045
2046
                            dout_,
                            q_,
2047
2048
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
2049
2050
2051
                            out_,
                            softmax_lse_,
                            dq_,
2052
2053
2054
                            dkv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else dkv_[0],
                            dkv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else dkv_[1],
                            *fa_backward_args_thd,
2055
2056
                            causal=False,
                            **fa_backward_kwargs,
2057
2058
2059
                        )
            else:
                if ctx.use_fused_attention:
2060
2061
2062
2063
                    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]]
2064
                    if attn_dbias is not None:
2065
                        aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2066
2067
2068
2069
2070
2071
2072
2073
                    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(
2074
                            q_part, fake_dtype=ctx.qkv_dtype, internal=True
2075
2076
                        )
                        k_part = ctx.QKV_quantizer.create_tensor_from_data(
2077
                            k_part, fake_dtype=ctx.qkv_dtype, internal=True
2078
2079
                        )
                        v_part = ctx.QKV_quantizer.create_tensor_from_data(
2080
                            v_part, fake_dtype=ctx.qkv_dtype, internal=True
2081
2082
                        )
                        out_part = ctx.O_quantizer.create_tensor_from_data(
2083
                            out_part, fake_dtype=ctx.qkv_dtype, internal=True
2084
2085
                        )
                        dout_part = ctx.dO_quantizer.create_tensor_from_data(
2086
                            dout_part, fake_dtype=dout_dtype, internal=True
2087
                        )
2088
2089
                        fp8_meta_kwargs["dp_quantizer"] = dP_quantizer_per_step[i]
                        fp8_meta_kwargs["dqkv_quantizer"] = dQKV_CP_quantizer_per_step[i]
2090
                    dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2091
                        ctx.max_seqlen_q,
2092
2093
2094
                        ctx.max_seqlen_kv,
                        cu_seqlens_q_per_step[cp_size - i - 1],
                        cu_seqlens_kv_per_step[cp_size - i - 1],
2095
2096
2097
2098
2099
2100
                        q_part,
                        k_part,
                        v_part,
                        out_part,
                        dout_part,
                        ctx.qkv_dtype,
2101
                        fused_attn_dqkv_dtype,
2102
                        aux_ctx_tensors,
2103
                        fused_attn_backend,
2104
2105
                        cu_seqlens_q_padded=cu_seqlens_q_padded,
                        cu_seqlens_kv_padded=cu_seqlens_kv_padded,
2106
2107
                        attn_scale=ctx.softmax_scale,
                        dropout=ctx.dropout_p,
2108
                        qkv_layout=qkv_layout,
2109
                        attn_mask_type=ctx.attn_mask_type,
2110
                        attn_bias_type=ctx.attn_bias_type,
2111
2112
                        deterministic=ctx.deterministic,
                        **fp8_meta_kwargs,
2113
                    )
2114
2115
2116
2117
2118
2119

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

2120
                else:
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
                    dq_ = torch.empty_like(q)
                    dkv_ = torch.empty_like(kv)
                    fa_backward_args_thd = []
                    if ctx.qkv_format == "thd":
                        fa_backward_args_thd = [
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
                            ctx.max_seqlen_q,
                            ctx.max_seqlen_kv,
                        ]
2131
                    if fa_utils.use_v3 or (fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus):
2132
                        fa_backward_kwargs["window_size"] = (-1, -1)
2133
                    elif fa_utils.v2_7_0_plus:
2134
2135
                        fa_backward_kwargs["window_size_left"] = -1
                        fa_backward_kwargs["window_size_right"] = -1
2136
                    if not fa_utils.use_v3:
2137
2138
                        fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                    flash_attn_bwd(
2139
2140
2141
2142
2143
                        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,
2144
2145
                        softmax_lse,
                        dq_,
2146
2147
2148
                        dkv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else dkv_[0],
                        dkv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else dkv_[1],
                        *fa_backward_args_thd,
2149
2150
                        causal=False,
                        **fa_backward_kwargs,
2151
2152
                    )

2153
2154
            if ctx.fp8:
                dq = dq_fp8[(rank + i + 1) % cp_size]
2155
2156
2157
            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]
2158
                dq_ = dq_.view(*dq.shape)
2159

2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
            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:
2171
                if i > (cp_size - rank - 1):
2172
                    dq.add_(dq_)
2173
2174
                elif i == (cp_size - rank - 1):
                    if rank == (cp_size - 1):
2175
2176
                        dq.copy_(dq_)
                    else:
2177
2178
2179
2180
2181
2182
                        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])
2183
                        elif ctx.qkv_format == "thd":
2184
                            tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "copy", "add")
2185
                elif i > 0:
2186
2187
2188
2189
                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].add_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].add_(dq_)
2190
                    elif ctx.qkv_format == "thd":
2191
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "none", "add")
2192
                else:
2193
2194
2195
2196
                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].copy_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].copy_(dq_)
2197
                    elif ctx.qkv_format == "thd":
2198
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "none", "copy")
2199
2200
2201
2202
2203
            else:
                if i == 0:
                    dq.copy_(dq_)
                else:
                    dq.add_(dq_)
2204

2205
            if attn_dbias is not None:
2206
                idx = (rank + i + 1) % cp_size
2207
                if i == (cp_size - 1) or not causal:
2208
                    # [b, np, sq, sk//cp] -> [b, np, sq, 2, sk//(2*cp)]
2209
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1] // 2)
2210
                    attn_dbias[..., idx, :].copy_(dbias_[..., 0, :])
2211
2212
                    attn_dbias[..., (2 * cp_size - idx - 1), :].copy_(dbias_[..., 1, :])
                elif i >= (cp_size - rank - 1):
2213
2214
2215
2216
                    # [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)]
2217
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1] // 2)
2218
                    attn_dbias_[..., 1, :, idx, :].copy_(dbias_[..., 0, :])
2219
                    attn_dbias_[..., 1, :, (2 * cp_size - idx - 1), :].copy_(dbias_[..., 1, :])
2220

2221
2222
2223
            # wait until dKV is received
            for req in send_recv_reqs:
                req.wait()
2224

2225
2226
2227
2228
2229
2230
2231
            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]
2232
            if ctx.use_fused_attention:
2233
                if ctx.qkv_format in ["bshd", "sbhd"]:
2234
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
                    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)
2248

2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
            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:
2260
                if i == (cp_size - 1):
2261
                    if rank == 0:
2262
2263
2264
2265
2266
2267
                        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, ...])
2268
                        elif ctx.qkv_format == "thd":
2269
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "add", "copy")
2270
2271
                    else:
                        dkv.add_(dkv_)
2272
2273
                elif i >= (cp_size - rank - 1):
                    if i == 0 and rank == (cp_size - 1):
2274
2275
2276
2277
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].copy_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].copy_(dkv_)
2278
                        elif ctx.qkv_format == "thd":
2279
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "copy", "none")
2280
                    else:
2281
2282
2283
2284
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].add_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].add_(dkv_)
2285
                        elif ctx.qkv_format == "thd":
2286
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "add", "none")
2287
2288
2289
2290
2291
                elif i > 0:
                    dkv.add_(dkv_)
                else:
                    dkv.copy_(dkv_)
            else:
2292
2293
2294
2295
2296
                if i == 0:
                    dkv.copy_(dkv_)
                else:
                    dkv.add_(dkv_)

2297
        if ctx.fp8 and ctx.use_fused_attention:
2298
2299
2300
            amax_cp_bwd = amax_per_step.amax(dim=1)
            ctx.dP_quantizer.amax = amax_cp_bwd[0]
            ctx.dQKV_CP_quantizer.amax = amax_cp_bwd[1]
2301
2302
2303
2304
            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:])
2305
2306
2307
2308
2309
2310
2311
            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]]
2312
2313
            dq, dkv = [x.sum(dim=0).to(dout_dtype) for x in [dq, dkv]]

2314
        if causal:
2315
2316
            if ctx.qkv_format == "bshd":
                # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
2317
                dq = dq.view(dq.shape[0], -1, *dq.shape[-2:])
2318
                # [2, b, 2, sk//2, np, hn] -> [2, b, sk, np, hn]
2319
                dkv = dkv.view(*dkv.shape[0:2], -1, *dkv.shape[-2:])
2320
2321
            elif ctx.qkv_format == "sbhd":
                # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
2322
                dq = dq.view(-1, *dq.shape[-3:])
2323
                # [2, 2, sk//2, b, np, hn] -> [2, sk, b, np, hn]
2324
2325
                dkv = dkv.view(dkv.shape[0], -1, *dkv.shape[-3:])

2326
2327
2328
        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)
2329

2330
        if ctx.fp8 and ctx.is_input_fp8:
2331
2332
            assert torch.uint8 not in [dq.dtype, dkv.dtype]
            dq, dkv = [ctx.dQKV_quantizer(x)._data for x in [dq, dkv]]
2333
2334
2335
        dk, dv = dkv[0], dkv[1]

        if cp_size_a2a > 1:
2336
            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_after_attn(cp_size_a2a, q.device)
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
            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]]

2351
2352
2353
        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)
2354
2355
        # converting torch.uint8 to float8tensor
        if ctx.fp8 and ctx.is_input_fp8:
2356
2357
2358
            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)
2359
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndKVP2P.backward")
2360

2361
2362
2363
        return (
            None,
            dq,
2364
2365
            dk,
            dv,
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
2377
            attn_dbias,
2378
2379
2380
2381
2382
            None,
            None,
            None,
            None,
            None,
2383
2384
            None,
            None,
2385
            None,
2386
            None,
2387
        )
2388
2389


2390
2391
def get_kv_seq_info_after_all_gather(
    local_chunk_id, cp_size, max_seqlen_q, max_seqlen_kv, window_size, causal
2392
):
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
    """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)
2415
2416
2417
2418


class AttnFuncWithCPAndKVAllGather(torch.autograd.Function):
    """
2419
2420
    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>`_.
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
2438
2439
2440
2441
2442
    """

    @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,
2443
2444
        cp_group,
        cp_stream,
2445
    ):
2446
        # pylint: disable=missing-function-docstring
2447
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndKVAllGather.forward")
2448
2449
2450
2451
2452
2453
        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)

2454
2455
        qkv_dtype = q.dtype

2456
2457
        causal = "causal" in attn_mask_type
        padding = "padding" in attn_mask_type
2458
        assert not padding, f"{attn_mask_type} mask type is not supported!"
2459
2460
2461
2462
2463
        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 (
2464
            use_fused_attention or fa_utils.v2_3_plus
2465
        ), "Sliding window attention only can work with FusedAttention or FlashAttention >= 2.3!"
2466

2467
        flash_attn_fwd = None
2468
2469
        if not use_fused_attention:
            fa_forward_kwargs = {"softmax_scale": softmax_scale}
2470
            if fa_utils.use_v3:
2471
2472
2473
2474
                if qkv_format == "thd":
                    flash_attn_fwd = _flash_attn_varlen_fwd_v3
                else:
                    flash_attn_fwd = _flash_attn_fwd_v3
2475
            else:
2476
2477
2478
2479
                if qkv_format == "thd":
                    flash_attn_fwd = _flash_attn_varlen_fwd
                else:
                    flash_attn_fwd = _flash_attn_fwd
2480
2481
                fa_forward_kwargs["dropout_p"] = dropout_p
                fa_forward_kwargs["return_softmax"] = False
2482
                if fa_utils.v2_4_plus:
2483
                    fa_forward_kwargs["alibi_slopes"] = None
2484
                if fa_utils.v2_5_7_plus and qkv_format == "thd":
2485
                    fa_forward_kwargs["block_table"] = None
2486
                if fa_utils.v2_6_0_plus:
2487
                    fa_forward_kwargs["softcap"] = 0.0
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498

        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)
2499
2500
        if use_fused_attention or qkv_format == "thd":
            cu_seqlens_q = cu_seqlens_q // (2 * cp_size)
2501
2502
2503
2504
        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
2505

2506
2507
2508
2509
        # [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]]
2510

2511
        # [s, b, np, hn] -> [cp, s, b, np, hn]
2512
2513
        k_ag, _ = gather_along_first_dim(k, cp_group)
        v_ag, _ = gather_along_first_dim(v, cp_group)
2514
2515

        # [cp, s, b, np, hn] -> [cp*2, s//2, b, np, hn]
2516
2517
        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:])
2518
        chunk_ids_for_kv_ag = get_seq_chunk_ids_for_reordering_before_attn(cp_size, k.device)
2519
2520
2521
2522
2523
2524
2525
2526
2527
        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]
2528
2529

        local_seq_chunk_ids = [rank, 2 * cp_size - rank - 1]
2530
2531
2532
        kv_seq_range_per_step = [None, None]
        window_size_per_step = [None, None]
        cu_seqlens_kv_per_step = [None, None]
2533
2534
2535
2536
2537
2538
2539
2540
        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]):
2541
2542
                    # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                    # or [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
2543
2544
2545
2546
2547
2548
2549
2550
2551
                    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,
2552
                        )
2553
2554
2555
2556
2557
2558
                    )
                    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
2559
                    if use_fused_attention or qkv_format == "thd":
2560
                        cu_seqlens_kv_per_step[i] = dpa_utils.get_full_cu_seqlens(
2561
2562
                            k.shape[1], max_seqlen_kv_, k.device
                        )
2563
2564
2565
                    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_]]
2566
2567
2568
2569
                    if use_fused_attention:
                        out_per_step[i], [softmax_lse_per_step[i], rng_states[i]] = fused_attn_fwd(
                            is_training,
                            max_seqlen_q,
2570
                            max_seqlen_kv_,
2571
                            cu_seqlens_q,
2572
                            cu_seqlens_kv_per_step[i],
2573
2574
2575
                            q_,
                            k_,
                            v_,
2576
                            qkv_dtype,
2577
2578
2579
2580
2581
2582
2583
2584
                            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,
2585
2586
                            cu_seqlens_kv_padded=cu_seqlens_kv_per_step[i],
                            window_size=window_size_per_step[i],
2587
2588
                        )
                    else:
2589
2590
2591
2592
2593
2594
2595
2596
                        fa_forward_args_thd = []
                        if qkv_format == "thd":
                            fa_forward_args_thd = [
                                cu_seqlens_q,
                                cu_seqlens_kv_per_step[i],
                                max_seqlen_q,
                                max_seqlen_kv_,
                            ]
2597
                        if fa_utils.use_v3 or (fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus):
2598
                            fa_forward_kwargs["window_size"] = window_size_per_step[i]
2599
                        elif fa_utils.v2_7_0_plus:
2600
2601
                            fa_forward_kwargs["window_size_left"] = window_size_per_step[i][0]
                            fa_forward_kwargs["window_size_right"] = window_size_per_step[i][1]
2602
2603
2604
2605
                        fa_outputs = flash_attn_fwd(
                            q_,
                            k_,
                            v_,
2606
                            *fa_forward_args_thd,
2607
2608
                            causal=causal,
                            **fa_forward_kwargs,
2609
                        )
2610
                        if not fa_utils.v2_7_0_plus:
2611
2612
                            out_per_step[i] = fa_outputs[4]
                            softmax_lse_per_step[i] = fa_outputs[5]
2613
                            if not fa_utils.use_v3:
2614
2615
2616
2617
                                rng_states[i] = fa_outputs[7]
                        else:
                            out_per_step[i] = fa_outputs[0]
                            softmax_lse_per_step[i] = fa_outputs[1]
2618
                            if not fa_utils.use_v3:
2619
                                rng_states[i] = fa_outputs[3]
2620
2621
2622
2623

            if i > 0:
                with torch.cuda.stream(flash_attn_streams[i - 1]):
                    if qkv_format == "bshd":
2624
                        out[:, i - 1].copy_(out_per_step[i - 1])
2625
                    elif qkv_format == "sbhd":
2626
                        out[i - 1].copy_(out_per_step[i - 1])
2627
2628
2629
2630
2631
2632
2633
2634
2635
2636
2637
2638
2639
2640
2641
2642
2643

        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,
2644
            *cu_seqlens_kv_per_step,
2645
2646
2647
2648
            *out_per_step,
            *softmax_lse_per_step,
            *rng_states,
        )
2649
2650

        ctx.qkv_dtype = qkv_dtype
2651
2652
        ctx.kv_seq_range_per_step = kv_seq_range_per_step
        ctx.window_size_per_step = window_size_per_step
2653
2654
2655
2656
2657
2658
2659
        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
2660
        ctx.attn_mask_type = attn_mask_type
2661
2662
        ctx.deterministic = deterministic
        ctx.use_fused_attention = use_fused_attention
2663
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndKVAllGather.forward")
2664
2665
2666
2667
        return out

    @staticmethod
    def backward(ctx, dout):
2668
        # pylint: disable=missing-function-docstring
2669
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndKVAllGather.backward")
2670
2671
2672
        cp_size = get_distributed_world_size(ctx.cp_group)
        rank = get_distributed_rank(ctx.cp_group)

2673
2674
2675
2676
2677
2678
        (*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]
2679
2680
        kv_seq_range_per_step = ctx.kv_seq_range_per_step
        window_size_per_step = ctx.window_size_per_step
2681

2682
        seq_dim = ctx.qkv_format.index("s")
2683
2684
        qkv_layout = ctx.qkv_format + "_" + ctx.qkv_format + "_" + ctx.qkv_format

2685
        dout = dout.view(q.shape)
2686
        dq = torch.empty_like(q)
2687
        dk = torch.zeros((k.shape[0] * cp_size, *k.shape[1:]), dtype=k.dtype, device=k.device)
2688
2689
2690
2691
2692
2693
2694
2695
2696
2697
        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()

2698
        # [s, b, np, hn] -> [cp, s, b, np, hn]
2699
2700
        k_ag, _ = gather_along_first_dim(k, ctx.cp_group)
        v_ag, _ = gather_along_first_dim(v, ctx.cp_group)
2701
2702

        # [cp, s, b, np, hn] -> [cp*2, s//2, b, np, hn]
2703
2704
        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:])
2705
        chunk_ids_for_kv_ag = get_seq_chunk_ids_for_reordering_before_attn(cp_size, k.device)
2706
2707
2708
2709
2710
2711
        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())
2712
2713
2714

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

2715
        flash_attn_bwd = None
2716
2717
        if not ctx.use_fused_attention:
            fa_backward_kwargs = {"softmax_scale": ctx.softmax_scale}
2718
            if fa_utils.use_v3:
2719
2720
2721
2722
                if ctx.qkv_format == "thd":
                    flash_attn_bwd = _flash_attn_varlen_bwd_v3
                else:
                    flash_attn_bwd = _flash_attn_bwd_v3
2723
2724
                fa_backward_kwargs["deterministic"] = ctx.deterministic
            else:
2725
2726
2727
2728
                if ctx.qkv_format == "thd":
                    flash_attn_bwd = _flash_attn_varlen_bwd
                else:
                    flash_attn_bwd = _flash_attn_bwd
2729
                fa_backward_kwargs["dropout_p"] = ctx.dropout_p
2730
                if fa_utils.v2_4_plus:
2731
                    fa_backward_kwargs["alibi_slopes"] = None
2732
                if fa_utils.v2_4_1_plus:
2733
                    fa_backward_kwargs["deterministic"] = ctx.deterministic
2734
                if fa_utils.v2_6_0_plus:
2735
                    fa_backward_kwargs["softcap"] = 0.0
2736
2737
2738
2739

        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]):
2740
2741
                    # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                    # or [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
2742
2743
2744
2745
2746
2747
2748
2749
2750
                    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_]]
2751
                    out_ = out_per_step[i]
2752
                    dout_ = dout.select(seq_dim, i).contiguous().view(out_.shape)
2753
2754
2755
2756
                    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,
2757
                            max_seqlen_kv,
2758
                            cu_seqlens_q,
2759
                            cu_seqlens_kv_per_step[i],
2760
2761
2762
2763
2764
                            q_,
                            k_,
                            v_,
                            out_,
                            dout_,
2765
                            ctx.qkv_dtype,
2766
                            TE_DType[dout.dtype],
2767
2768
2769
                            aux_ctx_tensors,
                            tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
2770
                            cu_seqlens_kv_padded=cu_seqlens_kv_per_step[i],
2771
2772
2773
2774
2775
                            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,
2776
2777
                            window_size=window_size_per_step[i],
                            deterministic=ctx.deterministic,
2778
2779
2780
2781
2782
                        )
                    else:
                        dq_per_step[i], dk_per_step[i], dv_per_step[i] = [
                            torch.empty_like(x) for x in [q_, k_, v_]
                        ]
2783
2784
2785
2786
2787
2788
2789
2790
                        fa_backward_args_thd = []
                        if ctx.qkv_format == "thd":
                            fa_backward_args_thd = [
                                cu_seqlens_q,
                                cu_seqlens_kv_per_step[i],
                                ctx.max_seqlen_q,
                                max_seqlen_kv,
                            ]
2791
                        if not fa_utils.use_v3:
2792
                            fa_backward_kwargs["rng_state"] = rng_states[i]
2793
                        if fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus:
2794
                            fa_backward_kwargs["window_size"] = window_size_per_step[i]
2795
                        if fa_utils.v2_7_0_plus:
2796
2797
                            fa_backward_kwargs["window_size_left"] = window_size_per_step[i][0]
                            fa_backward_kwargs["window_size_right"] = window_size_per_step[i][1]
2798
                        flash_attn_bwd(
2799
2800
2801
2802
2803
2804
2805
2806
2807
                            dout_,
                            q_,
                            k_,
                            v_,
                            out_,
                            softmax_lse_per_step[i],
                            dq_per_step[i],
                            dk_per_step[i],
                            dv_per_step[i],
2808
                            *fa_backward_args_thd,
2809
2810
                            causal="causal" in ctx.attn_mask_type,
                            **fa_backward_kwargs,
2811
2812
2813
2814
2815
                        )

            if i > 0:
                with torch.cuda.stream(flash_attn_streams[i - 1]):
                    if ctx.qkv_format == "bshd":
2816
                        dq[:, i - 1].copy_(dq_per_step[i - 1])
2817
                    elif ctx.qkv_format == "sbhd":
2818
2819
2820
2821
2822
2823
                        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]]
                    ]
2824
2825
2826
                    # wait until dkv update of last step is done
                    if i > 1:
                        flash_attn_streams[i - 1].wait_event(dkv_update_done)
2827
2828
2829
2830
2831
2832
                    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])
2833
2834
2835
2836
2837
                    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)

2838
2839
2840
        # [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:])
2841
        chunk_ids_for_kv_ag = get_seq_chunk_ids_for_reordering_after_attn(cp_size, dk.device)
2842
2843
2844
        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]
2845
2846
2847
2848
2849
        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)

2850
2851
2852
        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()
2853
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndKVAllGather.backward")
2854
2855
2856
2857
2858
2859
2860
2861
2862
2863
2864
2865
2866
2867
2868
2869
2870
2871
2872
2873
2874
2875
2876
2877
2878
2879
2880
2881
2882
2883
2884
2885
2886
2887
2888
2889
2890
2891
2892
2893
2894
2895
2896
2897
2898
2899
2900
2901
2902
2903
2904
2905
2906
2907
2908
2909

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


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

    @staticmethod
    def forward(
        ctx,
        is_training,
        q,
        k,
        v,
        cu_seqlens_q,
        cu_seqlens_kv,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
        dropout_p,
        softmax_scale,
        qkv_format,
        attn_mask_type,
        attn_bias_type,
        attn_bias,
        deterministic,
        use_fused_attention,
        window_size,
        fp8,
        fp8_meta,
        cp_group,
        cp_stream,
2910
        quantizers,
2911
    ):
2912
        # pylint: disable=missing-function-docstring
2913
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndQKVOA2A.forward")
2914
2915
2916
2917
        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)

        cp_size = get_distributed_world_size(cp_group)
2918
        qkv_dtype = q.dtype
2919
2920
2921
2922
2923
2924
2925
2926
2927
2928

        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
2929
            or fa_utils.v2_3_plus
2930
        ), "Sliding window attention only can work with FusedAttention or FlashAttention >= 2.3!"
2931

2932
        flash_attn_fwd = None
2933
2934
        if not use_fused_attention:
            fa_forward_kwargs = {"softmax_scale": softmax_scale}
2935
            if fa_utils.use_v3:
2936
2937
2938
2939
                if qkv_format == "thd":
                    flash_attn_fwd = _flash_attn_varlen_fwd_v3
                else:
                    flash_attn_fwd = _flash_attn_fwd_v3
2940
2941
                fa_forward_kwargs["window_size"] = window_size
            else:
2942
2943
2944
2945
                if qkv_format == "thd":
                    flash_attn_fwd = _flash_attn_varlen_fwd
                else:
                    flash_attn_fwd = _flash_attn_fwd
2946
2947
                fa_forward_kwargs["dropout_p"] = dropout_p
                fa_forward_kwargs["return_softmax"] = False
2948
                if fa_utils.use_v3 or (fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus):
2949
                    fa_forward_kwargs["window_size"] = window_size
2950
                elif fa_utils.v2_7_0_plus:
2951
2952
                    fa_forward_kwargs["window_size_left"] = window_size[0]
                    fa_forward_kwargs["window_size_right"] = window_size[1]
2953
                if fa_utils.v2_4_plus:
2954
                    fa_forward_kwargs["alibi_slopes"] = None
2955
                if fa_utils.v2_5_7_plus and qkv_format == "thd":
2956
                    fa_forward_kwargs["block_table"] = None
2957
                if fa_utils.v2_6_0_plus:
2958
                    fa_forward_kwargs["softcap"] = 0.0
2959
2960
2961
2962
2963
2964
2965
2966
2967
2968
2969
2970
2971
2972

        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!"

2973
        fused_attn_backend = None
2974
2975
        # "fp8_mha" decides outputs in fp8, while inputs are inferred from the real dtype
        is_input_fp8 = False
2976
2977
2978
        is_output_fp8 = False

        QKV_quantizer, O_quantizer, S_quantizer, dQKV_quantizer, dO_quantizer, dP_quantizer = (
2979
            dpa_utils.get_attention_quantizers(fp8, quantizers, cp_specific_quantizers=False)
2980
2981
2982
        )
        if fp8:
            if use_fused_attention:
2983
                fused_attn_backend = FusedAttnBackend["FP8"]
2984
2985
2986
2987
                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)
2988
                is_output_fp8 = fp8_meta is not None and fp8_meta["recipe"].fp8_mha
2989
                if is_input_fp8:
2990
                    QKV_quantizer = q._quantizer
2991
2992
2993
2994
                    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
2995
                    q, k, v = [QKV_quantizer(x)._data for x in [q_f16, k_f16, v_f16]]
2996
                fp8_meta_kwargs = {}
2997
2998
                fp8_meta_kwargs["s_quantizer"] = S_quantizer
                fp8_meta_kwargs["o_quantizer"] = O_quantizer  # partial result quantizer
2999
3000
3001
3002
3003
3004
3005
            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"]

3006
        chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_before_attn(cp_size, q.device)
3007
3008
3009
3010
        q, k, v = flash_attn_a2a_communicate(
            [q, k, v], chunk_ids_for_a2a, seq_dim, cp_size, cp_group, cp_stream, True
        )

3011
        if fp8 and not is_input_fp8 and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
3012
            q_f16, k_f16, v_f16 = q, k, v
3013
            q, k, v = [QKV_quantizer(x)._data for x in [q_f16, k_f16, v_f16]]
3014
3015
3016

        batch_size = q.shape[batch_dim]
        if use_fused_attention:
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
            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
                )
3028
3029
3030
3031
3032
3033
            out, aux_ctx_tensors = fused_attn_fwd(
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
3034
3035
3036
3037
                q_part,
                k_part,
                v_part,
                qkv_dtype,
3038
3039
3040
3041
3042
3043
3044
3045
3046
3047
3048
3049
                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,
            )
3050
3051
            if fp8:
                out = out._data
3052
        else:
3053
3054
3055
3056
3057
3058
3059
3060
            fa_forward_args_thd = []
            if qkv_format == "thd":
                fa_forward_args_thd = [
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
                ]
3061
            fa_outputs = flash_attn_fwd(
3062
3063
3064
                q,
                k,
                v,
3065
                *fa_forward_args_thd,
3066
                causal=causal,
3067
                **fa_forward_kwargs,
3068
            )
3069
            if not fa_utils.v2_7_0_plus:
3070
                out, softmax_lse = fa_outputs[4], fa_outputs[5]
3071
                rng_state = fa_outputs[7] if not fa_utils.use_v3 else None
3072
3073
            else:
                out, softmax_lse = fa_outputs[0], fa_outputs[1]
3074
                rng_state = fa_outputs[3] if not fa_utils.use_v3 else None
3075
3076
            aux_ctx_tensors = [softmax_lse, rng_state]

3077
        chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_after_attn(cp_size, out.device)
3078
3079
3080
3081
3082
3083
3084
3085
3086
3087
3088
3089
3090
        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:
3091
            if is_output_fp8:
3092
3093
                out_fp8 = O_quantizer.create_tensor_from_data(
                    out, fake_dtype=qkv_dtype, internal=False
3094
3095
                )
                out_ret = out_fp8
3096
                out = out_fp8._data
3097
            else:
3098
                out_fp8 = O_quantizer.create_tensor_from_data(
3099
                    out, fake_dtype=qkv_dtype, internal=True
3100
                )
3101
                out_f16 = out_fp8.dequantize(dtype=qkv_dtype)
3102
3103
3104
3105
                out_ret = out_f16
        else:
            out_ret = out

3106
        if not fp8 or int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
3107
            q_save, k_save, v_save, out_save = q, k, v, out
3108
3109
3110
3111
3112
3113
3114
3115
3116
        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
3117

3118
        tensors_to_save, tensor_objects = prepare_for_saving(
3119
3120
3121
3122
3123
3124
3125
3126
3127
3128
            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,
        )
3129
3130
3131
3132
3133
3134
3135
3136
3137
3138
3139
        ctx.save_for_backward(*tensors_to_save)
        ctx.tensor_objects = tensor_objects

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

3140
3141
3142
3143
3144
3145
3146
3147
3148
3149
3150
3151
3152
3153
3154
        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
3155
3156
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
3157
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndQKVOA2A.forward")
3158
3159
3160
3161
        return out_ret

    @staticmethod
    def backward(ctx, dout):
3162
        # pylint: disable=missing-function-docstring
3163
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndQKVOA2A.backward")
3164
3165
        cp_size = get_distributed_world_size(ctx.cp_group)

3166
3167
3168
3169
3170
3171
3172
3173
3174
3175
3176
        (
            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)
3177
3178
3179
3180
3181

        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")

3182
        dout_dtype = dout.dtype
3183
3184
        fused_attn_backend = None
        fused_attn_dqkv_dtype = None
3185
3186
3187
        if ctx.fp8:
            if ctx.use_fused_attention:
                fused_attn_backend = FusedAttnBackend["FP8"]
3188
                if ctx.is_output_fp8:
3189
                    assert isinstance(dout, Float8Tensor), "dout must be Float8Tensors for FP8 MHA!"
3190
                    ctx.dO_quantizer = dout._quantizer
3191
                else:
3192
3193
3194
                    dout = ctx.dO_quantizer(dout)
                fused_attn_dqkv_dtype = dout._fp8_dtype
                dout = dout._data
3195
                fp8_meta_kwargs = {}
3196
3197
3198
3199
                fp8_meta_kwargs["s_quantizer"] = ctx.S_quantizer
                fp8_meta_kwargs["dp_quantizer"] = ctx.dP_quantizer
                fp8_meta_kwargs["dqkv_quantizer"] = ctx.dQKV_quantizer

3200
3201
3202
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
            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]]
3219
3220
            if ctx.use_fused_attention:
                fp8_meta_kwargs = {}
3221
                fused_attn_dqkv_dtype = TE_DType[dout_dtype]
3222
3223
3224
3225
3226
3227
                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)

3228
        chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_before_attn(cp_size, out.device)
3229
3230
3231
        out, dout = flash_attn_a2a_communicate(
            [out, dout], chunk_ids_for_a2a, seq_dim, cp_size, ctx.cp_group, ctx.cp_stream, True
        )
3232
3233
3234
3235
3236
3237
3238
3239
3240
        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)
3241

3242
        flash_attn_bwd = None
3243
3244
        if not ctx.use_fused_attention:
            fa_backward_kwargs = {"softmax_scale": ctx.softmax_scale}
3245
            if fa_utils.use_v3:
3246
3247
3248
3249
                if ctx.qkv_format == "thd":
                    flash_attn_bwd = _flash_attn_varlen_bwd_v3
                else:
                    flash_attn_bwd = _flash_attn_bwd_v3
3250
3251
3252
                fa_backward_kwargs["window_size"] = ctx.window_size
                fa_backward_kwargs["deterministic"] = ctx.deterministic
            else:
3253
3254
3255
3256
                if ctx.qkv_format == "thd":
                    flash_attn_bwd = _flash_attn_varlen_bwd
                else:
                    flash_attn_bwd = _flash_attn_bwd
3257
                fa_backward_kwargs["dropout_p"] = ctx.dropout_p
3258
                if fa_utils.use_v3 or (fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus):
3259
                    fa_backward_kwargs["window_size"] = ctx.window_size
3260
                elif fa_utils.v2_7_0_plus:
3261
3262
                    fa_backward_kwargs["window_size_left"] = ctx.window_size[0]
                    fa_backward_kwargs["window_size_right"] = ctx.window_size[1]
3263
                if fa_utils.v2_4_plus:
3264
                    fa_backward_kwargs["alibi_slopes"] = None
3265
                if fa_utils.v2_4_1_plus:
3266
                    fa_backward_kwargs["deterministic"] = ctx.deterministic
3267
                if fa_utils.v2_6_0_plus:
3268
                    fa_backward_kwargs["softcap"] = 0.0
3269
3270

        if ctx.use_fused_attention:
3271
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
3282
3283
3284
3285
3286
3287
3288
3289
3290
            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(
3291
                    dout_part, fake_dtype=dout_dtype, internal=True
3292
3293
                )

3294
3295
3296
3297
3298
            dq, dk, dv, _ = fused_attn_bwd(
                ctx.max_seqlen_q,
                ctx.max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
3299
3300
3301
3302
3303
3304
                q_part,
                k_part,
                v_part,
                out_part,
                dout_part,
                ctx.qkv_dtype,
3305
3306
3307
3308
3309
3310
3311
3312
3313
3314
3315
3316
3317
3318
                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,
            )
3319
3320
3321
3322
            if ctx.fp8:
                dq = dq._data
                dk = dk._data
                dv = dv._data
3323
3324
3325
        else:
            softmax_lse, rng_state = aux_ctx_tensors
            dq, dk, dv = [torch.empty_like(x) for x in [q, k, v]]
3326
3327
3328
3329
3330
3331
3332
3333
            fa_backward_args_thd = []
            if ctx.qkv_format == "thd":
                fa_backward_args_thd = [
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    ctx.max_seqlen_q,
                    ctx.max_seqlen_kv,
                ]
3334
            if not fa_utils.use_v3:
3335
3336
                fa_backward_kwargs["rng_state"] = rng_state
            flash_attn_bwd(
3337
3338
3339
3340
3341
3342
3343
3344
3345
                dout,
                q,
                k,
                v,
                out,
                softmax_lse,
                dq,
                dk,
                dv,
3346
                *fa_backward_args_thd,
3347
3348
                causal=causal,
                **fa_backward_kwargs,
3349
3350
            )

3351
        chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_after_attn(cp_size, q.device)
3352
3353
3354
3355
        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
        )

3356
        if ctx.qkv_format == "bshd":
3357
            dq, dk, dv = [x.view(ctx.batch_size, -1, *x.shape[-2:]) for x in [dq, dk, dv]]
3358
        elif ctx.qkv_format == "sbhd":
3359
3360
3361
            dq, dk, dv = [x.view(-1, ctx.batch_size, *x.shape[-2:]) for x in [dq, dk, dv]]

        if ctx.fp8:
3362
3363
3364
3365
3366
3367
3368
3369
3370
            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
            )
3371
            if not ctx.is_input_fp8:
3372
                dq, dk, dv = [x.dequantize(dtype=dout_dtype) for x in [dq, dk, dv]]
3373
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndQKVOA2A.backward")
3374
3375
3376
3377
3378
3379
3380
3381
3382
3383
3384
3385
3386
3387
3388
3389
3390
3391
3392
3393
3394
3395
3396

        return (
            None,
            dq,
            dk,
            dv,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
3397
3398
3399
            None,
            None,
            None,
3400
            None,
3401
3402
3403
        )


3404
def attn_forward_func_with_cp(
3405
3406
3407
3408
3409
    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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) -> 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]
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        out = AttnFuncWithCPAndKVP2P.apply(*args)
    elif cp_comm_type == "all_gather":
        args.pop(5)
        args.pop(8)
        args += [window_size, cp_group, cp_stream]
        out = AttnFuncWithCPAndKVAllGather.apply(*args)
    elif cp_comm_type == "a2a":
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        args += [window_size, fp8, fp8_meta, cp_group, cp_stream, quantizers]
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        out = AttnFuncWithCPAndQKVOA2A.apply(*args)
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    else:
        raise ValueError(f"Unsupported communication type: {cp_comm_type}!")

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    return out


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class _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
        )
3684

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    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
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        qkv_layout: str = "sbh3d",
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        cu_seqlens_q: Optional[torch.Tensor] = None,  # pylint: disable=unused-argument
        cu_seqlens_kv: Optional[torch.Tensor] = None,  # pylint: disable=unused-argument
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        attn_mask_type: str = "causal",
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        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
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        window_size: Optional[Tuple[int, int]] = None,
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        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
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        alibi_slopes: Optional[torch.Tensor] = None,
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    ) -> torch.Tensor:
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        """Unfused attention fprop"""
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        assert (
            qkv_layout in QKVLayouts
        ), f"UnfusedDotProductAttention does not support qkv_layout = {qkv_layout}!"
        qkv_format = "".join([i for i in qkv_layout.split("_")[0] if i.isalpha()])
        if qkv_format == "bshd":
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            # convert to sbhd and use sbhd implementation for now
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            query_layer, key_layer, value_layer = [
                x.transpose(0, 1) for x in [query_layer, key_layer, value_layer]
            ]
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        batch_size, max_seqlen_q, max_seqlen_kv = (
            query_layer.shape[1],
            query_layer.shape[0],
            key_layer.shape[0],
        )
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        attn_mask_type, attention_mask, actual_seqlens_q, actual_seqlens_kv = (
            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]
3750
        query_layer = query_layer.reshape(output_size[2], output_size[0] * output_size[1], -1)
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        # [sk, b, np, hn] -> [sk, b * np, hn]
        key_layer = key_layer.reshape(output_size[3], output_size[0] * output_size[1], -1)

        # preallocting result tensor: [b * np, sq, sk]
        matmul_result = torch.empty(
            output_size[0] * output_size[1],
            output_size[2],
            output_size[3],
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            dtype=query_layer.dtype,
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            device=torch.cuda.current_device(),
        )

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        scale = self.softmax_scale
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        if apply_qk_layer_scaling:
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            scale /= self.layer_number
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        # Raw attention scores. [b * np, sq, sk]
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        if core_attention_bias_type == "no_bias":
            matmul_result = torch.baddbmm(
                matmul_result,
                query_layer.transpose(0, 1),  # [b * np, sq, hn]
                key_layer.transpose(0, 1).transpose(1, 2),  # [b * np, hn, sk]
                beta=0.0,
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                alpha=scale,
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            ).view(*output_size)
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        elif core_attention_bias_type == "pre_scale_bias":
            assert core_attention_bias is not None, "core_attention_bias should not be None!"
            matmul_result = torch.bmm(
                query_layer.transpose(0, 1),  # [b * np, sq, hn]
                key_layer.transpose(0, 1).transpose(1, 2),  # [b * np, hn, sk]
            )
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            matmul_result = matmul_result.view(*output_size) + core_attention_bias
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            matmul_result *= scale
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        elif core_attention_bias_type in ["post_scale_bias", "alibi"]:
            if core_attention_bias_type == "post_scale_bias":
                assert core_attention_bias is not None, "core_attention_bias should not be None!"
            if core_attention_bias_type == "alibi":
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                _, core_attention_bias = dpa_utils.get_alibi(
                    _alibi_cache,
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                    output_size[1],
                    output_size[2],
                    output_size[3],
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                    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,
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                    alibi_slopes=alibi_slopes,
                    bottom_right_alignment=attn_mask_type not in ["causal", "padding_causal"],
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                )
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            matmul_result = torch.baddbmm(
                matmul_result,
                query_layer.transpose(0, 1),  # [b * np, sq, hn]
                key_layer.transpose(0, 1).transpose(1, 2),  # [b * np, hn, sk]
                beta=0.0,
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                alpha=scale,
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            )
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            matmul_result = (matmul_result.view(*output_size) + core_attention_bias).to(
                dtype=query_layer.dtype
3809
            )
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        # attention scores and attention mask [b, np, sq, sk]
        softmax_scale = self.layer_number if apply_qk_layer_scaling else None
3813
        attention_probs = self.scale_mask_softmax(
3814
            matmul_result, attention_mask, attn_mask_type, softmax_scale
3815
        )
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        # 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)

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        # 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]
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        value_layer = value_layer.reshape(value_layer.size(0), output_size[0] * output_size[1], -1)
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        # change view [b * np, sq, sk]
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        attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
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        # 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)

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        if qkv_format == "sbhd":
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            # [b, np, sq, hn] --> [sq, b, np, hn]
            context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
3851

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

3855
        if qkv_format == "bshd":
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            # [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)
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        return context_layer


class _PrepareQKVForFA(torch.autograd.Function):
    """This class converts QKV from interleaved (s, b, ...) layout
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    to separate contiguous q, k, v tensors in (b, s, ...) layout."""
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    @staticmethod
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    def forward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
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        value_layer: torch.Tensor,
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    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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        # pylint: disable=missing-function-docstring
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        # 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
3888
3889
3890
3891
    def backward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        dq: torch.Tensor,
        dk: torch.Tensor,
3892
        dv: torch.Tensor,
3893
    ) -> Tuple[Union[torch.Tensor, None], ...]:
3894
        # pylint: disable=missing-function-docstring
3895
3896
3897
3898
        dqkv = tex.fa_prepare_bwd(dq, dk, dv)
        dq, dk, dv = split_tensor_along_dim(dqkv, -1, 3)
        return dq, dk, dv

3899

3900
class FlashAttention(torch.nn.Module):
3901
    """Dot product attention, using HazyResearch flash-attn package:
3902
    https://github.com/Dao-AILab/flash-attention
3903
3904
3905
3906
    """

    def __init__(
        self,
3907
        softmax_scale: float,
3908
3909
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
3910
3911
        attention_type: str = "self",
        layer_number: Optional[int] = None,
3912
        deterministic: bool = False,
3913
3914
3915
    ) -> None:
        super().__init__()

3916
        if fa_utils.is_installed:
3917
            assert (
3918
3919
                fa_utils.version >= fa_utils.version_required
            ), f"FlashAttention minimum version {fa_utils.version_required} is required."
3920
            assert (
3921
3922
                fa_utils.version <= fa_utils.max_version
            ), f"FlashAttention maximum version {fa_utils.max_version} is supported."
3923

3924
        self.softmax_scale = softmax_scale
3925
3926
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_dropout = attention_dropout
3927
3928
        self.attention_type = attention_type
        self.layer_number = 1 if layer_number is None else layer_number
3929
        self.deterministic = deterministic
3930
        self.logger = logging.getLogger("FlashAttention")
3931
        self.logger.setLevel(attn_log._log_level)
3932
        if not self.logger.hasHandlers():
3933
            self.logger.addHandler(attn_log._stream_handler)
3934
3935
3936
3937
3938
3939

    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
3940
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
3941
3942
3943
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
3944
3945
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
3946
        attn_mask_type: str = "causal",
3947
        window_size: Optional[Tuple[int, int]] = None,
3948
        alibi_slopes: Optional[torch.Tensor] = None,
3949
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
3950
        cp_global_ranks: List[int] = None,
3951
        cp_stream: torch.cuda.Stream = None,
3952
        cp_comm_type: str = "p2p",
3953
3954
        fp8: bool = False,
        fp8_meta: Optional[Dict[str, Any]] = None,
3955
        quantizers=None,
3956
3957
3958
    ) -> torch.Tensor:
        """flash-attn fprop"""

3959
3960
3961
3962
        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."
3963
3964
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
3965
        ), "FlashAttention currently only supports CUDA tensors."
3966
3967
        assert (
            qkv_layout in QKVLayouts
3968
        ), f"FlashAttention does not support qkv_layout = {qkv_layout}!"
3969

3970
3971
3972
3973
3974
3975
        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)
3976
        context_parallel = cp_size > 1
3977

3978
        qkv_format = "".join([i for i in qkv_layout.split("_")[0] if i.isalpha()])
3979

3980
3981
3982
3983
3984
3985
3986
3987
3988
3989
3990
3991
3992
        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 = [
3993
                        x.transpose(0, 1) for x in (query_layer, key_layer, value_layer)
3994
                    ]
3995
            if context_parallel:
3996
                query_layer, key_layer, value_layer = [
3997
3998
3999
4000
4001
                    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 = [
4002
                    x.transpose(0, 1)
4003
4004
                    for x in (query_layer._data, key_layer._data, value_layer._data)
                ]
4005
                query_layer, key_layer, value_layer = [
4006
                    Float8Tensor.make_like(x, data=x._data, shape=x._data.shape)
4007
4008
                    for x in (query_layer, key_layer, value_layer)
                ]
4009
            if context_parallel:
4010
4011
                query_layer._data, key_layer._data, value_layer._data = [
                    x.contiguous() for x in (query_layer._data, key_layer._data, value_layer._data)
4012
                ]
4013

4014
        batch_size = query_layer.shape[0]
4015

4016
        if qkv_format in ["sbhd", "bshd"]:
4017
            max_seqlen_q, max_seqlen_kv = query_layer.shape[1], key_layer.shape[1]
4018
4019
            max_seqlen_q *= cp_size
            max_seqlen_kv *= cp_size
4020
4021
4022

            if "padding" in attn_mask_type:
                assert not context_parallel, "Padding mask not supported with context parallelism!"
4023
4024
                # [b * s, h, d]
                query_layer, key_layer, value_layer = [
4025
                    x.reshape(x.shape[0] * x.shape[1], *x.shape[2:])
4026
4027
4028
4029
4030
4031
4032
                    for x in [query_layer, key_layer, value_layer]
                ]

                if self.attention_type == "self":
                    assert (
                        max_seqlen_q == max_seqlen_kv
                    ), "Maximum sequence length for Q and KV should be the same."
4033
                    if cu_seqlens_q is None:
4034
4035
4036
                        assert (
                            attention_mask is not None
                        ), "Please provide attention_mask for padding!"
4037
4038
4039
                        cu_seqlens_q, indices_q = dpa_utils.get_cu_seqlens_and_indices(
                            attention_mask
                        )
4040
                    else:
4041
                        indices_q = dpa_utils.get_indices(max_seqlen_q, cu_seqlens_q)
4042
                    cu_seqlens_kv = cu_seqlens_q
4043
                    query_layer, key_layer, value_layer = dpa_utils.PackTensors.apply(
4044
                        indices_q, query_layer, key_layer, value_layer
4045
4046
                    )
                else:
4047
                    if cu_seqlens_q is None or cu_seqlens_kv is None:
4048
4049
4050
                        assert (
                            attention_mask is not None
                        ), "Please provide attention_mask for padding!"
4051
4052
4053
4054
4055
4056
                        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]
                        )
4057
                    else:
4058
4059
4060
4061
4062
4063
                        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
                    )
4064
            else:
4065
4066
                # Cumulative sequence lengths for unpadded data
                if cu_seqlens_q is None:
4067
                    cu_seqlens_q = dpa_utils.get_full_cu_seqlens(
4068
4069
4070
4071
4072
                        batch_size,
                        max_seqlen_q,
                        query_layer.device,
                    )
                if cu_seqlens_kv is None:
4073
                    cu_seqlens_kv = dpa_utils.get_full_cu_seqlens(
4074
4075
4076
4077
                        batch_size,
                        max_seqlen_kv,
                        key_layer.device,
                    )
4078
4079
4080
4081
        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!"
4082
4083
4084
4085
4086
4087
            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()
4088

4089
4090
4091
        if context_parallel and all(
            not isinstance(x, Float8Tensor) for x in [query_layer, key_layer, value_layer]
        ):
4092
4093
4094
            assert (
                alibi_slopes is None
            ), "Alibi slope bias addition is not supported with context parallelism."
4095
            with self.attention_dropout_ctx():
4096
                output = attn_forward_func_with_cp(
4097
4098
4099
4100
4101
4102
4103
4104
                    self.training,
                    query_layer,
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
4105
4106
                    cu_seqlens_q if qkv_format == "thd" else None,
                    cu_seqlens_kv if qkv_format == "thd" else None,
4107
                    self.attention_dropout if self.training else 0.0,
4108
4109
4110
                    cp_group,
                    cp_global_ranks,
                    cp_stream,
4111
                    cp_comm_type,
4112
                    softmax_scale=self.softmax_scale,
4113
                    qkv_format="bshd" if qkv_format == "sbhd" else qkv_format,
4114
                    attn_mask_type=attn_mask_type,
4115
                    deterministic=self.deterministic,
4116
                    window_size=window_size,
4117
                    quantizers=quantizers,
4118
                    pad_between_seqs=False,
4119
4120
                )
        else:
4121
4122

            from .cpu_offload import CPUOffloadEnabled
4123

4124
4125
4126
4127
4128
4129
            if CPUOffloadEnabled:
                tensor_list = [query_layer, key_layer, value_layer, cu_seqlens_q, cu_seqlens_kv]
                for tensor in tensor_list:
                    if tensor is not None:
                        tensor.activation_offloading = True

4130
            with self.attention_dropout_ctx():
4131
                fa_optional_forward_kwargs = {}
4132
                if fa_utils.v2_3_plus:
4133
                    fa_optional_forward_kwargs["window_size"] = window_size
4134
                if fa_utils.v2_4_plus:
4135
                    fa_optional_forward_kwargs["alibi_slopes"] = alibi_slopes
4136
                if fa_utils.v2_4_1_plus:
4137
                    fa_optional_forward_kwargs["deterministic"] = self.deterministic
4138
4139
                fa_optional_forward_args_thd = []
                if qkv_format in ["bshd", "sbhd"] and "padding" not in attn_mask_type:
4140
                    func = flash_attn_func if not fa_utils.use_v3 else flash_attn_func_v3
4141
                else:
4142
                    if fa_utils.v2_5_7_plus:
4143
                        fa_optional_forward_kwargs["block_table"] = None
4144
                    func = (
4145
                        flash_attn_varlen_func if not fa_utils.use_v3 else flash_attn_varlen_func_v3
4146
4147
4148
4149
4150
                    )
                    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)
4151
                if fa_utils.use_v3:
4152
4153
4154
                    fa_3_optional_forward_kwargs = {}
                    fa_3_optional_forward_kwargs["window_size"] = window_size
                    fa_3_optional_forward_kwargs["deterministic"] = self.deterministic
4155
                    if fp8:
4156
                        QKV_quantizer = quantizers["scaling_fwd"][META_QKV]
4157
                        torch_dtype = get_fp8_torch_dtype(fp8_meta["recipe"], fprop_tensor=True)
4158
                        torch_orig_dtype = query_layer.dtype
4159
4160
4161
4162
4163
4164
4165
4166
4167
4168
4169

                        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

4170
4171
4172
4173
4174
                        # "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."
4175
                        if not isinstance(query_layer, Float8Tensor):
4176
                            query_layer, key_layer, value_layer = (
4177
                                QKV_quantizer(x) for x in [query_layer, key_layer, value_layer]
4178
                            )
4179
4180
                        fa_3_optional_forward_kwargs["descale_q"] = (
                            query_layer._scale_inv.unsqueeze(0)
4181
                        )
4182
4183
                        fa_3_optional_forward_kwargs["descale_k"] = key_layer._scale_inv.unsqueeze(
                            0
4184
                        )
4185
4186
                        fa_3_optional_forward_kwargs["descale_v"] = (
                            value_layer._scale_inv.unsqueeze(0)
4187
                        )
4188
4189
4190
                        query_layer, key_layer, value_layer = (
                            convert_to_torch_float8(x, torch_dtype)
                            for x in [query_layer, key_layer, value_layer]
4191
                        )
4192
4193
4194
4195
4196
4197
4198
4199
4200
4201
4202
                    try:
                        output, _ = func(
                            query_layer,
                            key_layer,
                            value_layer,
                            *fa_optional_forward_args_thd,
                            softmax_scale=self.softmax_scale,
                            causal="causal" in attn_mask_type,
                            **fa_3_optional_forward_kwargs,
                        )
                    except TypeError as e:
4203
                        if fa_utils.v3_0_0_beta:
4204
4205
4206
4207
                            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"
4208
                                + fa_utils.v3_installation_steps,
4209
4210
4211
4212
4213
4214
4215
4216
                            ) + 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)
4217
                else:
4218
4219
4220
4221
4222
4223
4224
4225
4226
                    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,
4227
                    )
4228

4229
        if qkv_format in ["sbhd", "bshd"] and "padding" in attn_mask_type:
4230
            output = dpa_utils.UnpackTensor.apply(indices_q, batch_size * max_seqlen_q, output)
4231
4232
4233
4234
4235
4236
4237
4238
4239
4240
4241
4242
4243
4244
4245
4246
4247
4248
4249
4250
4251
4252
4253
4254
4255
4256
4257
4258
4259
4260
4261
4262
4263
4264
4265
4266
4267
4268
4269
4270
4271
4272
4273
4274
4275
4276
4277
4278
4279
4280
4281
4282

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

        return output.contiguous()


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

    num_tensors = len(tensors)
    new_shape = list(tensors[0].shape)
    new_shape.insert(dim, num_tensors)
    if isinstance(tensors[0], Float8Tensor):
        new_stride = list(tensors[0]._data.stride())
        new_stride.insert(dim, int(new_stride[dim - 1] / num_tensors))
        combined_tensor = torch.Tensor().to(device=tensors[0].device, dtype=tensors[0]._data.dtype)
        combined_tensor.set_(
            tensors[0]._data.untyped_storage(),
            tensors[0]._data.storage_offset(),
            new_shape,
            new_stride,
        )
        combined_tensor = Float8Tensor.make_like(tensors[0], data=combined_tensor, shape=new_shape)
    else:
        new_stride = list(tensors[0].stride())
        new_stride.insert(dim, int(new_stride[dim - 1] / num_tensors))
        combined_tensor = torch.Tensor().to(device=tensors[0].device, dtype=tensors[0].dtype)
        combined_tensor.set_(
            tensors[0].untyped_storage(), tensors[0].storage_offset(), new_shape, new_stride
4283
4284
        )

4285
4286
    return combined_tensor

4287

4288
4289
4290
4291
class FusedAttnFunc(torch.autograd.Function):
    """Function for FusedAttention with separate Q, K, V tensors"""

    @staticmethod
4292
4293
4294
4295
4296
4297
4298
    def forward(
        ctx,
        is_training,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q,
        cu_seqlens_kv,
4299
4300
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
4301
4302
4303
4304
4305
4306
4307
4308
4309
4310
        q,
        k,
        v,
        attn_bias,
        attn_scale,
        dropout_p,
        fast_zero_fill,
        qkv_layout,
        attn_bias_type,
        attn_mask_type,
4311
        window_size,
4312
4313
4314
4315
4316
        rng_gen,
        fused_attention_backend,
        use_FAv2_bwd,
        fp8,
        fp8_meta,
4317
        quantizers,
4318
        deterministic,
4319
    ):
4320
        # pylint: disable=missing-function-docstring
4321
        # "fp8_mha" decides outputs in fp8, while inputs are inferred from the real dtype
4322
        is_input_fp8 = False
4323
        is_output_fp8 = fp8_meta["recipe"].fp8_mha if "recipe" in fp8_meta else False
4324
4325
4326
4327

        # 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
4328
4329
4330
        fake_dtype = q.dtype

        QKV_quantizer, O_quantizer, S_quantizer, dQKV_quantizer, dO_quantizer, dP_quantizer = (
4331
            dpa_utils.get_attention_quantizers(fp8, quantizers, cp_specific_quantizers=False)
4332
        )
4333
4334
        if fp8:
            fused_attention_backend = FusedAttnBackend["FP8"]
4335
4336
4337
            assert isinstance(k, q.__class__) and isinstance(
                v, q.__class__
            ), "q, k, and v must have the same type."
4338

4339
            is_input_fp8 = isinstance(q, Float8Tensor)
4340
            q_fp8, k_fp8, v_fp8 = None, None, None
4341
            if is_input_fp8:
4342
                q_fp8, k_fp8, v_fp8 = q, k, v
4343
4344
            else:
                # 1: qkv packed, 2: kv packed, 3: qkv separate
4345
                qkv_group = len(qkv_layout.split("_"))
4346
4347
4348
4349
4350
4351
4352
4353
4354
4355
4356
4357
4358
4359
4360
4361
4362
4363
4364
4365
                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
4366
            # q_fp8, k_fp8, v_fp8, out_fp8: torch.float8_e4m3fn
4367
            out_fp8, aux_ctx_tensors = fused_attn_fwd(
4368
4369
4370
4371
4372
4373
4374
4375
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q_fp8,
                k_fp8,
                v_fp8,
4376
                fake_dtype,
4377
4378
                fused_attention_backend,
                attn_bias,
4379
4380
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
4381
4382
                S_quantizer,
                O_quantizer,
4383
4384
4385
4386
4387
4388
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
4389
                window_size,
4390
4391
                rng_gen,
            )
4392
            if is_output_fp8:
4393
                out_ret = out_fp8
4394
            else:
4395
                out_ret = out_fp8.dequantize().view(out_fp8.shape)
4396
4397
            # is_output_fp8 = False: out_save.dtype = torch.float16 or torch.bfloat16
            # is_output_fp8 = True:  out_save.dtype = torch.float8_e4m3fn
4398
4399
            out_save = out_ret

4400
            if not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
4401
                # 1: qkv packed, 2: kv packed, 3: qkv separate
4402
4403
4404
4405
4406
4407
                if is_input_fp8:
                    qkv_group = len(qkv_layout.split("_"))
                    if qkv_group == 1:
                        dim = qkv_layout.find("3")
                        qkv = _combine_tensors([q, k, v], dim)
                        qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
4408
4409
                        qkv_no_fp8 = qkv_c.dequantize().view(qkv.shape)
                        q, k, v = _SplitAlongDim.apply(qkv_no_fp8, dim, [1, 1, 1], True)
4410
                    if qkv_group == 2:
4411
                        q = q.dequantize()
4412
4413
4414
                        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])
4415
4416
                        kv_no_fp8 = kv.dequantize()
                        k, v = _SplitAlongDim.apply(kv_no_fp8, dim, [1, 1], True)
4417
                    if qkv_group == 3:
4418
4419
4420
                        q = q.dequantize()
                        k = k.dequantize()
                        v = v.dequantize()
4421
                if is_output_fp8:
4422
4423
4424
                    out_save = out_fp8.dequantize()

            fp8_tensors = (q_fp8, k_fp8, v_fp8, out_fp8)
4425
        else:
4426
            # q, k, v, out_ret: torch.float16 or torch.bfloat16
4427
            out_ret, aux_ctx_tensors = fused_attn_fwd(
4428
4429
4430
4431
4432
4433
4434
4435
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q,
                k,
                v,
4436
                fake_dtype,
4437
4438
                fused_attention_backend,
                attn_bias,
4439
4440
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
4441
4442
                None,  # s_quantizer
                None,  # o_quantizer
4443
4444
4445
4446
4447
4448
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
4449
                window_size,
4450
4451
                rng_gen,
            )
4452
            out_save = out_ret
4453
            fp8_tensors = (None, None, None, None)
4454

4455
4456
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))

4457
        from .cpu_offload import CPUOffloadEnabled
4458

4459
        if CPUOffloadEnabled:
4460
4461
4462
4463
4464
4465
4466
            if ctx.fp8:
                tensor_list = fp8_tensors
            else:
                tensor_list = [q, k, v, out_save]

            tensor_list.extend(aux_ctx_tensors)

4467
            qkv_layout = "sbhd_sbhd_sbhd"
4468
4469
4470
4471
            for tensor in tensor_list:
                if tensor is not None:
                    tensor.activation_offloading = True

4472
4473
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
4474
        qkvo_tensors = (q, k, v, out_save) if not ctx.fp8 else (None, None, None, None)
4475
4476
        tensors_to_save, tensor_objects = prepare_for_saving(
            *fp8_tensors,
4477
4478
4479
            *qkvo_tensors,
            cu_seqlens_q,
            cu_seqlens_kv,
4480
4481
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
4482
4483
            *aux_ctx_tensors,
        )
4484
4485
        ctx.save_for_backward(*tensors_to_save)
        ctx.tensor_objects = tensor_objects
4486
        ctx.fp8_meta = fp8_meta
4487
4488
4489
4490
4491
4492

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

4493
4494
4495
4496
4497
4498
4499
4500
        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
4501
        ctx.window_size = window_size
4502
        ctx.fused_attention_backend = (
4503
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
4504
        )
4505
        ctx.use_FAv2_bwd = use_FAv2_bwd
4506
        ctx.deterministic = deterministic
4507

4508
        return out_ret
4509
4510
4511

    @staticmethod
    def backward(ctx, d_out):
4512
        # pylint: disable=missing-function-docstring
4513
        if ctx.is_output_fp8:
4514
4515
4516
            assert isinstance(
                d_out, Float8Tensor
            ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
4517

4518
4519
4520
4521
4522
        # 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

4523
        d_out = d_out.contiguous()
4524
        (
4525
4526
4527
4528
            q_fp8,
            k_fp8,
            v_fp8,
            out_fp8,
4529
4530
4531
4532
4533
4534
            q,
            k,
            v,
            out,
            cu_seqlens_q,
            cu_seqlens_kv,
4535
4536
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
4537
4538
4539
4540
4541
            *other_tensors,
        ) = restore_from_saved(ctx.tensor_objects, ctx.saved_tensors)

        aux_ctx_tensors = other_tensors

4542
4543
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
4544
        rest = [None]
4545
        if ctx.use_FAv2_bwd:
4546
            softmax_lse, rng_state = aux_ctx_tensors
4547
4548
4549
            dq = torch.empty_like(q)
            dk = torch.empty_like(k)
            dv = torch.empty_like(v)
4550
            d_out, q, k, v, out = [maybe_contiguous(x) for x in (d_out, q, k, v, out)]
4551
            flash_attn_cuda_bwd(
4552
4553
4554
4555
4556
4557
4558
4559
4560
4561
4562
4563
4564
4565
4566
4567
4568
4569
4570
                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,
4571
            )
4572
4573
4574
            dq = dq[..., : d_out.shape[-1]]
            dk = dk[..., : d_out.shape[-1]]
            dv = dv[..., : d_out.shape[-1]]
4575
        else:
4576
4577
            with torch.cuda.nvtx.range("_FusedAttn"):
                if ctx.fp8:
4578
                    if ctx.is_output_fp8:
4579
4580
                        d_out_fp8 = d_out
                    else:
4581
                        d_out_fp8 = ctx.dO_quantizer(d_out)
4582
4583
4584
                    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
4585
                    dq_fp8, dk_fp8, dv_fp8, *rest = fused_attn_bwd(
4586
4587
4588
4589
4590
4591
4592
4593
4594
                        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,
4595
4596
                        fake_dtype,
                        dqkv_dtype,
4597
                        aux_ctx_tensors,
4598
                        ctx.fused_attention_backend,
4599
4600
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
4601
4602
4603
                        ctx.S_quantizer,
                        ctx.dP_quantizer,
                        ctx.dQKV_quantizer,
4604
4605
4606
4607
4608
4609
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
4610
4611
                        ctx.window_size,
                        ctx.deterministic,
4612
                    )
4613

4614
4615
                    # is_input_fp8 = False: dq, dk, dv: torch.float16 or torch.bfloat16
                    # is_input_fp8 = True:  dq, dk, dv: torch.float8_e5m2
4616
                    if not ctx.is_input_fp8:
4617
                        qkv_group = len(ctx.qkv_layout.split("_"))
4618
                        if qkv_group == 1:
4619
                            dim = ctx.qkv_layout.find("3")
4620
4621
                            dqkv_fp8_data = _combine_tensors(
                                [dq_fp8._data, dk_fp8._data, dv_fp8._data], dim
4622
                            )
4623
4624
4625
4626
4627
                            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)
4628
                        if qkv_group == 2:
4629
                            dq = dq_fp8.dequantize()
4630
4631
4632
4633
4634
                            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]
                            )
4635
4636
                            dkv = dkv_c_fp8.dequantize()
                            dk, dv = _SplitAlongDim.apply(dkv, dim, [1, 1], True)
4637
                        if qkv_group == 3:
4638
4639
4640
4641
4642
                            dq = dq_fp8.dequantize()
                            dk = dk_fp8.dequantize()
                            dv = dv_fp8.dequantize()
                    else:
                        dq, dk, dv = dq_fp8, dk_fp8, dv_fp8
4643
                else:
4644
4645
                    if isinstance(d_out, QuantizedTensor):
                        d_out = d_out.dequantize()
4646
4647
                    dqkv_dtype = TE_DType[d_out.dtype]
                    # q, k, v, out, d_out, dq, dk, dv: torch.float16 or torch.bfloat16
4648
                    dq, dk, dv, *rest = fused_attn_bwd(
4649
4650
4651
4652
4653
4654
4655
4656
4657
                        ctx.max_seqlen_q,
                        ctx.max_seqlen_kv,
                        cu_seqlens_q,
                        cu_seqlens_kv,
                        q,
                        k,
                        v,
                        out,
                        d_out,
4658
4659
                        fake_dtype,
                        dqkv_dtype,
4660
                        aux_ctx_tensors,
4661
                        ctx.fused_attention_backend,
4662
4663
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
4664
4665
4666
4667
4668
4669
4670
4671
4672
                        None,
                        None,
                        None,
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
4673
4674
                        ctx.window_size,
                        ctx.deterministic,
4675
                    )
4676

4677
4678
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
4679
4680
4681
4682
4683
4684
4685
4686
4687
4688
4689
4690
4691
4692
4693
4694
4695
4696
4697
4698
4699
4700
4701
4702
4703
4704
            return (
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                dq,
                dk,
                dv,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
4705
4706
                None,
                None,
4707
            )
4708
        # else, return (dqkv, dbias)
4709
4710
4711
4712
4713
4714
4715
4716
4717
4718
4719
4720
4721
4722
4723
4724
4725
4726
4727
4728
4729
4730
4731
4732
4733
        return (
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            dq,
            dk,
            dv,
            rest[0],
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
4734
4735
            None,
            None,
4736
            None,
4737
        )
4738

4739

4740
class FusedAttention(torch.nn.Module):
4741
4742
4743
4744
4745
4746
4747
4748
4749
    """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:

4750
4751
4752
4753
    | backend       | 1                       | 2                              |
    | flash based   | no                      | yes                            |
    | cuDNN based   | yes                     | yes                            |
    | qkv dtype     | fp16/bf16               | fp16/bf16                      |
4754
    | attn_type     | self/cross              | self/cross                     |
4755
    | qkv_layout    |                         |                                |
4756
    |  - (q,k,v)    | sb3hd, bs3hd            | sb3hd, bs3hd, sbh3d, bsh3d     |
4757
    |               | sbhd_sb2hd, bshd_bs2hd  | sbhd_sb2hd, bshd_bs2hd         |
4758
4759
    |               | bshd_bshd_bshd          | sbhd_sbh2d, bshd_bsh2d         |
    |               |                         | sbhd_sbhd_sbhd, bshd_bshd_bshd |
4760
4761
    | mask_type     | causal/padding/no_mask  | causal/padding/no_mask         |
    | bias_type     | post_scale_bias/no_bias | post_scale_bias/alibi/no_bias  |
4762
    | dropout       | yes                     | yes                            |
4763
4764
    | max_seqlen    | <=512, multiple of 64   | any, multiple of 64            |
    | head_dim      | 64                      | <=128, multiple of 8           |
4765
    | output dtype  | fp16/bf16               | fp16/bf16                      |
4766
4767
4768
4769
    """

    def __init__(
        self,
4770
        softmax_scale: float,
4771
4772
4773
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
        attention_type: str = "self",
4774
4775
        layer_number: Optional[int] = None,
        deterministic: bool = False,
4776
4777
4778
    ) -> None:
        super().__init__()

4779
        self.softmax_scale = softmax_scale
4780
4781
4782
        self.attention_dropout = attention_dropout
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_type = attention_type
4783
4784
4785
        self.use_FAv2_bwd = os.getenv(
            "NVTE_FUSED_ATTN_USE_FAv2_BWD", "0"
        ) == "1" and get_device_compute_capability() == (9, 0)
4786
        self.layer_number = 1 if layer_number is None else layer_number
4787
        self.deterministic = deterministic
4788

4789
        def remove_extra_states_check(self, incompatible_keys):  # pylint: disable=unused-argument
4790
4791
            """
            Temporarily remove fused_attention._extra_state as a missing key
4792
            or an unexpected key when loading Transformer Engine checkpoints.
4793
4794
            Please store FP8 metadata as DotProductAttention's _extra_state,
            rather than FusedAttention's _extra_state. This hook will be
4795
            phased out in Transformer Engine 2.0.
4796
4797
            """
            for key in incompatible_keys.missing_keys:
4798
                if "fused_attention._extra_state" in key:
4799
                    incompatible_keys.missing_keys.remove(key)
4800
4801
4802
4803
4804
4805
4806
            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."
                    )
4807

4808
4809
        self.register_load_state_dict_post_hook(remove_extra_states_check)

4810
    @no_torch_dynamo()
4811
4812
4813
4814
4815
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
4816
4817
4818
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
4819
4820
        cu_seqlens_q_padded: Optional[torch.Tensor] = None,
        cu_seqlens_kv_padded: Optional[torch.Tensor] = None,
4821
4822
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
4823
        attn_mask_type: str = "causal",
4824
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
4825
        window_size: Optional[Tuple[int, int]] = None,
4826
        fused_attention_backend: tex.NVTE_Fused_Attn_Backend = tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend,
4827
4828
4829
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
        fast_zero_fill: bool = True,
4830
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
4831
4832
        cp_global_ranks: List[int] = None,
        cp_stream: torch.cuda.Stream = None,
4833
        cp_comm_type: str = "p2p",
4834
4835
        fp8: bool = False,
        fp8_meta: Optional[Dict[str, Any]] = None,
4836
        quantizers=None,
4837
        pad_between_seqs: bool = False,
4838
4839
    ) -> torch.Tensor:
        """fused attention fprop"""
4840
4841
4842
        assert (
            fused_attention_backend != tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend
        ), "No fused attention backend supports this input combination!"
4843
4844
4845
4846
        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."
4847
4848
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
4849
        ), "FusedAttention only supports CUDA tensors."
4850
4851
        assert (
            qkv_layout in QKVLayouts
4852
        ), f"FusedAttention does not support qkv_layout = {qkv_layout}!"
4853

4854
4855
4856
4857
4858
4859
        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)
4860
        context_parallel = cp_size > 1
4861

4862
        qkv_format = "".join([i for i in qkv_layout.split("_")[0] if i.isalpha()])
4863

4864
4865
        if qkv_format in ["sbhd", "bshd"]:
            if qkv_format == "sbhd":
4866
                batch_size, max_seqlen_q, max_seqlen_kv = (
4867
4868
4869
4870
4871
                    query_layer.shape[1],
                    query_layer.shape[0],
                    key_layer.shape[0],
                )
            if qkv_format == "bshd":
4872
                batch_size, max_seqlen_q, max_seqlen_kv = (
4873
4874
4875
4876
                    query_layer.shape[0],
                    query_layer.shape[1],
                    key_layer.shape[1],
                )
4877
4878
            max_seqlen_q *= cp_size
            max_seqlen_kv *= cp_size
4879
            if "padding" in attn_mask_type:
4880
4881
                assert not context_parallel, "Padding mask not supported with context parallelism!"

4882
4883
4884
4885
4886
                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!"
                        )
4887
                    if self.attention_type == "self":
4888
                        cu_seqlens_q = dpa_utils.get_cu_seqlens(attention_mask)
4889
                        cu_seqlens_kv = cu_seqlens_q
4890
                    else:
4891
4892
                        cu_seqlens_q = dpa_utils.get_cu_seqlens(attention_mask[0])
                        cu_seqlens_kv = dpa_utils.get_cu_seqlens(attention_mask[1])
4893
            else:
4894
                if cu_seqlens_q is None:
4895
                    cu_seqlens_q = dpa_utils.get_full_cu_seqlens(
4896
4897
4898
4899
4900
                        batch_size,
                        max_seqlen_q,
                        query_layer.device,
                    )
                if cu_seqlens_kv is None:
4901
                    cu_seqlens_kv = dpa_utils.get_full_cu_seqlens(
4902
4903
4904
4905
                        batch_size,
                        max_seqlen_kv,
                        key_layer.device,
                    )
4906
4907
4908
        if qkv_format == "thd":
            assert (
                max_seqlen_q is not None
4909
4910
4911
                and max_seqlen_kv is not None
                and cu_seqlens_q is not None
                and cu_seqlens_kv is not None
4912
            ), "max_seqlen_q/kv and cu_seqlens_q/kv can not be None when qkv_format is thd!"
4913

4914
        if qkv_format == "thd" and (cu_seqlens_q_padded is None or cu_seqlens_kv_padded is None):
4915
4916
            cu_seqlens_q_padded = cu_seqlens_q
            cu_seqlens_kv_padded = cu_seqlens_kv
4917

4918
4919
4920
4921
4922
        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)
        )
4923

4924
4925
4926
4927
4928
4929
4930
4931
4932
4933
4934
        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!"
            )

4935
        if context_parallel:
4936
            assert (
4937
4938
                fp8
                or fused_attention_backend == tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen
4939
4940
4941
4942
4943
4944
4945
            ), 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)
            ]
4946
4947
4948
            with self.attention_dropout_ctx():
                output = attn_forward_func_with_cp(
                    self.training,
4949
4950
4951
4952
4953
4954
4955
                    query_layer,
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
4956
4957
                    cu_seqlens_q_padded,
                    cu_seqlens_kv_padded,
4958
                    self.attention_dropout if self.training else 0.0,
4959
4960
4961
                    cp_group,
                    cp_global_ranks,
                    cp_stream,
4962
                    cp_comm_type,
4963
                    softmax_scale=self.softmax_scale,
4964
                    qkv_format=qkv_format,
4965
                    attn_mask_type=attn_mask_type,
4966
4967
                    attn_bias_type=core_attention_bias_type,
                    attn_bias=core_attention_bias,
4968
                    deterministic=self.deterministic,
4969
                    use_fused_attention=True,
4970
                    window_size=window_size,
4971
4972
                    fp8=fp8,
                    fp8_meta=fp8_meta,
4973
                    quantizers=quantizers,
4974
                    pad_between_seqs=pad_between_seqs,
4975
4976
                )
        else:
4977
4978
4979
4980
4981
4982
4983
            with self.attention_dropout_ctx():
                output = FusedAttnFunc.apply(
                    self.training,
                    max_seqlen_q,
                    max_seqlen_kv,
                    cu_seqlens_q,
                    cu_seqlens_kv,
4984
4985
                    cu_seqlens_q_padded,
                    cu_seqlens_kv_padded,
4986
4987
4988
4989
4990
4991
4992
4993
4994
4995
                    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,
4996
                    window_size,
4997
4998
4999
5000
5001
                    None,  # rng_gen
                    fused_attention_backend,
                    use_FAv2_bwd,
                    fp8,
                    fp8_meta,
5002
                    quantizers,
5003
                    self.deterministic,
5004
                )
5005

5006
5007
        # ...hd -> ...(hd)
        return output.view(*output.shape[:-2], -1)
5008
5009


5010
class DotProductAttention(TransformerEngineBaseModule):
5011
5012
5013
5014
5015
5016
    """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::

5017
        Argument :attr:`attention_mask` in the `forward` call is only used when
5018
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
5019
5020
5021

    .. warning::

5022
        FlashAttention uses a non-deterministic algorithm for optimal performance. To observe
5023
        deterministic behavior at the cost of performance, use FlashAttention version >= `2.4.1`
5024
5025
        and set the environment variable :attr:`NVTE_ALLOW_NONDETERMINISTIC_ALGO=0`. In order
        to disable`flash-attn` entirely, set :attr:`NVTE_FLASH_ATTN=0`.
5026

5027
5028
5029
5030
5031
5032
5033
    .. 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>`_).


5034
5035
5036
5037
    Parameters
    ----------
    num_attention_heads : int
                         number of attention heads in the transformer layer.
5038
5039
5040
    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.
5041
5042
5043
5044
5045
5046
5047
5048
    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`.
5049
5050
    attention_dropout: float, default = 0.0
                      dropout probability for the dropout op during multi-head attention.
5051
    attn_mask_type: str, default = `causal`
5052
                   type of attention mask passed into softmax operation, options are "`no_mask`",
5053
5054
5055
5056
5057
5058
5059
5060
5061
                   "`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
5062
                   "`padding_causal`" and "`padding_causal_bottom_right`", Transformer Engine
5063
5064
5065
5066
5067
5068
5069
5070
5071
5072
5073
5074
5075
5076
                   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].
5077
5078
5079
5080
    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
5081
5082
5083
                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
5084
                be overridden by :attr:`window_size` in `forward` as well.
5085
5086
    attention_type: str, default = `self`
                   type of attention, either "`self`" and "`cross`".
5087
5088
5089
    layer_number: int, default = `None`
                 layer number of the current `DotProductAttention` when multiple such modules
                 are concatenated, for instance in consecutive transformer blocks.
5090
5091
5092
    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,
5093
               `h` the number of heads, `d` head size, and `t` the total number of tokens
5094
5095
5096
5097
5098
               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.
5099
               For that, please use `get_qkv_layout` to gain the layout information.
5100
5101
    softmax_scale: Optional[float], default = `None`
                softmax scale for the attention scores. If `None`, defaults to
5102
                `1.0/math.sqrt(kv_channels if isinstance(kv_channels, int) else kv_channels[0])`.
5103
5104
5105
5106
5107
5108
5109
5110
5111

    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.
5112
    cp_group : Union[ProcessGroup, List[ProcessGroup]], default = `None`
5113
              context parallel process group.
5114
5115
5116
              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.
5117
5118
5119
5120
5121
5122
5123
    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.
5124
    cp_comm_type : str, default = `p2p`
5125
                  inter-gpu communication type for context parallelism.
5126
                  Can be "p2p" or "all_gather" or "a2a" or "a2a+p2p".
5127
5128
5129
5130
5131
5132
                  "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.
5133
5134
5135
                  "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).
5136
5137
5138
5139
5140
    """

    def __init__(
        self,
        num_attention_heads: int,
5141
        kv_channels: Union[int, Tuple[int, int]],
5142
        num_gqa_groups: Optional[int] = None,
5143
        attention_dropout: float = 0.0,
5144
        qkv_format: str = "sbhd",
5145
        attn_mask_type: str = "causal",
5146
        window_size: Optional[Tuple[int, int]] = None,
5147
5148
5149
5150
5151
        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,
5152
        attention_type: str = "self",
5153
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
5154
        cp_global_ranks: List[int] = None,
5155
        cp_stream: torch.cuda.Stream = None,
5156
        cp_comm_type: str = "p2p",
5157
        softmax_scale: Optional[float] = None,
5158
5159
5160
    ) -> None:
        super().__init__()

5161
        self.logger = logging.getLogger("DotProductAttention")
5162
        self.logger.setLevel(attn_log._log_level)
5163
        if not self.logger.hasHandlers():
5164
            self.logger.addHandler(attn_log._stream_handler)
5165
        self.qkv_format = qkv_format
5166
        attn_mask_type = attn_mask_type.replace(",", "_")
5167
5168
        if attn_mask_type == "causal_padding":
            attn_mask_type = "padding_causal"
5169
        self.attn_mask_type = attn_mask_type
5170
        self.window_size = dpa_utils.check_set_window_size(attn_mask_type, window_size)
5171
5172
5173
5174
5175
5176
5177
        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)
5178
        self.get_rng_state_tracker = get_rng_state_tracker
5179
        self.num_attention_heads = num_attention_heads
5180
        self.layer_number = 1 if layer_number is None else layer_number
5181
5182
5183
        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream
5184
        self.cp_comm_type = cp_comm_type
5185

5186
5187
5188
5189
5190
5191
        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]
        )
5192

5193
        self.num_gqa_groups = num_attention_heads if num_gqa_groups is None else num_gqa_groups
5194
        self.num_gqa_groups_per_partition = int(self.num_gqa_groups // self.tp_size)
5195

5196
5197
5198
        assert (
            num_attention_heads % self.num_gqa_groups == 0
        ), "The number of attention heads must be divisible by the number of GQA groups!"
5199

5200
        self.rng_states_tracker = None
5201
5202
5203
        if sequence_parallel or get_rng_state_tracker is None:
            attention_dropout_ctx = nullcontext
        else:
5204
5205
5206
            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
5207

5208
        if softmax_scale is None:
5209
5210
5211
            softmax_scale = 1.0 / math.sqrt(
                kv_channels if isinstance(kv_channels, int) else kv_channels[0]
            )
5212

5213
5214
5215
        self.deterministic = (
            not bool(int(os.getenv("NVTE_ALLOW_NONDETERMINISTIC_ALGO", "1")))
            or torch.are_deterministic_algorithms_enabled()
5216
        )
5217
5218
5219
5220
5221
5222
5223
5224
5225
5226
5227
5228
5229
5230
5231
5232
5233
5234
5235
        # 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"
5236

5237
        assert attention_type in AttnTypes, f"attention_type {attention_type} not supported"
5238
5239
5240
5241

        self.attention_type = attention_type
        self.attention_dropout = attention_dropout

5242
5243
5244
5245
5246
        attn_kwargs = {
            "attention_dropout": attention_dropout,
            "attention_dropout_ctx": attention_dropout_ctx,
        }

5247
5248
5249
5250
5251
5252
5253
        self.flash_attention = FlashAttention(
            softmax_scale,
            attention_type=attention_type,
            layer_number=layer_number,
            deterministic=self.deterministic,
            **attn_kwargs,
        )
5254

5255
        # Instantiating three types since use of flash-attn and FusedAttention
5256
        # might be ruled out due to forward inputs.
5257
5258
5259
5260
5261
5262
5263
        self.fused_attention = FusedAttention(
            softmax_scale,
            attention_type=attention_type,
            layer_number=layer_number,
            deterministic=self.deterministic,
            **attn_kwargs,
        )
5264

5265
        self.unfused_attention = UnfusedDotProductAttention(
5266
5267
5268
5269
            softmax_scale,
            attention_type=attention_type,
            **attn_kwargs,
            layer_number=layer_number,
5270
        )
5271

5272
5273
5274
        def remove_extra_states_check(self, incompatible_keys):  # pylint: disable=unused-argument
            """
            Temporarily remove core_attention._extra_state as a missing key
5275
5276
            when loading older Transformer Engine checkpoints. Will phase out
            this hook in Transformer Engine 2.0.
5277
5278
5279
5280
5281
5282
5283
            """
            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)

5284
5285
5286
5287
5288
5289
5290
5291
5292
5293
5294
5295
5296
5297
5298
5299
5300
5301
5302
5303
5304
5305
    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
        )

5306
5307
5308
5309
    def _checkpointed_attention_forward(
        self,
        attention_func: Callable,
        *forward_args: Tuple[torch.Tensor, ...],
5310
        **forward_kwargs: Dict[str, Any],
5311
5312
5313
    ) -> torch.Tensor:
        """Forward method with activation checkpointing."""

5314
5315
        def custom_forward(*input_args, **input_kwargs):
            return attention_func(*input_args, **input_kwargs)
5316
5317
5318

        hidden_states = checkpoint(
            custom_forward,
5319
5320
5321
            distribute_saved_activations=False,
            get_rng_state_tracker=self.get_rng_state_tracker,
            tp_group=self.tp_group,
5322
            *forward_args,
5323
            **forward_kwargs,
5324
5325
5326
5327
        )

        return hidden_states

5328
5329
    def set_context_parallel_group(
        self,
5330
        cp_group: Union[dist_group_type, List[dist_group_type], None],
5331
5332
        cp_global_ranks: List[int],
        cp_stream: torch.cuda.Stream,
5333
        cp_comm_type: str = "p2p",
5334
    ) -> None:
5335
5336
5337
5338
5339
5340
        """
        Set the context parallel attributes for the given
        module before executing the forward pass.

        Parameters
        ----------
5341
        cp_group : Union[ProcessGroup, List[ProcessGroup]]
5342
                  context parallel process group.
5343
5344
5345
                  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.
5346
5347
5348
5349
        cp_global_ranks : List[int]
                         list of global ranks in the context group.
        cp_stream : torch.cuda.Stream
                   cuda stream for context parallel execution.
5350
        cp_comm_type : str, default = `p2p`
5351
                      inter-gpu communication type for context parallelism.
5352
                      Can be "p2p" or "all_gather" or "a2a" or "a2a+p2p".
5353
5354
5355
5356
5357
5358
                      "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.
5359
5360
5361
                      "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).
5362
        """
5363
5364
5365
        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream
5366
        self.cp_comm_type = cp_comm_type
5367

5368
    @no_torch_dynamo(recursive=False)
5369
5370
5371
5372
5373
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
5374
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
5375
5376
5377
        qkv_format: Optional[str] = None,
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
5378
5379
        cu_seqlens_q_padded: Optional[torch.Tensor] = None,
        cu_seqlens_kv_padded: Optional[torch.Tensor] = None,
5380
5381
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
5382
        attn_mask_type: Optional[str] = None,
5383
        window_size: Optional[Tuple[int, int]] = None,
5384
        checkpoint_core_attention: bool = False,
5385
5386
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
5387
        alibi_slopes: Optional[torch.Tensor] = None,
5388
        fast_zero_fill: bool = True,
5389
        inference_params: Optional[InferenceParams] = None,
5390
        pad_between_seqs: Optional[bool] = None,
5391
5392
5393
5394
5395
5396
    ) -> torch.Tensor:
        """
        Dot Product Attention Layer.

        .. note::

5397
5398
            Argument :attr:`attention_mask` is only used when :attr:`attn_mask_type`
            includes '"padding"' or `"arbitrary"`.
5399

5400
5401
        .. note::

5402
5403
5404
5405
5406
5407
5408
5409
5410
5411
5412
5413
5414
            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,
5415
            and FusedAttention backend if applicable, to use. Transformer Engine prioritizes
5416
5417
5418
5419
            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
5420
5421
            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
5422
            optimizations in FusedAttention. When unset, Transformer Engine determines the code path
5423
5424
            based on its internal logic. These optimizations trade memory for performance
            and should be used with care.
5425

5426
5427
5428
5429
5430
5431
5432
5433
5434
5435
5436
5437
5438
5439
5440
5441
5442
5443
5444
5445
5446
5447
5448
5449
5450
5451
5452
5453
5454
5455
5456
5457
5458
5459
5460
5461
5462
5463
5464
5465
5466
5467
5468
5469
5470
5471
5472
5473
5474
5475
5476
5477
5478
5479
        .. 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`}.

5480
5481
5482
5483
5484
5485
5486
5487
        Parameters
        ----------
        query_layer : torch.Tensor
                     Query tensor.
        key_layer : torch.Tensor
                   Key tensor.
        value_layer : torch.Tensor
                     Value tensor.
5488
5489
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]],
             default = `None`. Boolean tensor(s) used to mask out attention softmax input.
5490
             It should be `None` for causal masks and "`no_mask`". For padding masks, it should be
5491
5492
             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]
5493
5494
5495
5496
             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.
5497
5498
5499
        qkv_format: str, default = `None`
                   If provided, overrides :attr:`qkv_format` from initialization.
        cu_seqlens_q: Optional[torch.Tensor], default = `None`
5500
                   Cumulative sum of sequence lengths (without offset) in a batch for `query_layer`,
5501
                   with shape [batch_size + 1] and dtype torch.int32.
5502
                   See :ref:`note<cu_seqlens note>` for more details.
5503
        cu_seqlens_kv: Optional[torch.Tensor], default = `None`
5504
5505
                   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.
5506
                   See :ref:`note<cu_seqlens note>` for more details.
5507
5508
5509
5510
5511
        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`.
5512
                   See :ref:`note<cu_seqlens note>` for more details.
5513
5514
5515
5516
5517
        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`.
5518
                   See :ref:`note<cu_seqlens note>` for more details.
5519
5520
        max_seqlen_q: Optional[int], default = `None`
                      Maximum sequence length in `query_layer`.
5521
                      See :ref:`note<max_seqlen note>` for more details.
5522
5523
        max_seqlen_kv: Optional[int], default = `None`
                       Maximum sequence length in `key_layer` and `value_layer`.
5524
                       See :ref:`note<max_seqlen note>` for more details.
5525
5526
5527
5528
5529
5530
5531
        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.
5532
        window_size: Optional[Tuple[int, int]], default = `None`
5533
                    Sliding window size for local attention.
5534
5535
5536
5537
5538
        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.
5539
        core_attention_bias_type: str, default = `no_bias`
5540
                    Bias type, {`no_bias`, `pre_scale_bias`, `post_scale_bias`, `alibi`}
5541
        core_attention_bias: Optional[torch.Tensor], default = `None`
5542
5543
                    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.
5544
5545
5546
5547
        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.
5548
        fast_zero_fill: bool, default = `True`
5549
                    Whether to use the fast path to set output tensors to 0 or not.
5550
5551
5552
5553
5554
5555
5556
5557
5558
5559
        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.
5560
5561
5562
        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.
5563
        """
5564

5565
5566
5567
5568
5569
5570
5571
5572
5573
        with self.prepare_forward(
            query_layer,
            num_gemms=3,
            allow_non_contiguous=True,
        ) as query_layer:
            if self.fp8:
                if self.fp8_meta["recipe"].fp8_mha:
                    if not self.fp8_meta["recipe"].fp8_dpa:
                        self.fp8_meta["recipe"].fp8_dpa = True
5574
                        self.logger.warning(
5575
5576
5577
                            """Forcing fp8_meta["recipe"].fp8_dpa=True due to """
                            """fp8_meta["recipe"].fp8_mha=True"""
                        )
5578
5579
5580
5581
5582
5583
5584
5585
5586
5587
5588

            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."""
5589

5590
5591
5592
            assert (
                query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
            ), "DotProductAttention only supports CUDA tensors."
5593
5594
5595
            assert (
                query_layer.dtype == key_layer.dtype and query_layer.dtype == value_layer.dtype
            ), "Queries, keys and values must have the same data type!"
5596
5597
5598
            assert (
                key_layer.shape[:-1] == value_layer.shape[:-1]
            ), "Keys and values must have the same batch size, sequence length and number of heads!"
5599
5600
5601
5602
5603
5604
5605
5606
            assert (
                key_layer.shape[-1] == self.hidden_size_per_attention_head_k
            ), f"Keys have head_dim = {key_layer.shape[-1]}, "
            "but expected head_dim = {self.hidden_size_per_attention_head_k}!"
            assert (
                value_layer.shape[-1] == self.hidden_size_per_attention_head_v
            ), f"Values have head_dim = {value_layer.shape[-1]}, "
            "but expected head_dim = {self.hidden_size_per_attention_head_v}!"
5607

5608
5609
5610
            if qkv_format is None:
                qkv_format = self.qkv_format

5611
5612
5613
5614
5615
5616
            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"
5617
            assert (
5618
5619
5620
5621
5622
5623
                attn_mask_type in AttnMaskTypes
            ), f"Attention mask type {attn_mask_type} is not supported!"
            if qkv_format == "thd":
                assert (
                    "padding" in attn_mask_type
                ), "Attention mask type must be padding or padding_causal for qkv_format=thd!"
5624

5625
5626
            if window_size is None:
                window_size = self.window_size
5627
            window_size = dpa_utils.check_set_window_size(attn_mask_type, window_size)
5628

5629
5630
5631
5632
5633
5634
5635
            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."
5636

5637
5638
            if inference_params is not None:
                assert self.layer_number is not None, "Layer number must be set!"
5639

5640
5641
5642
5643
5644
                # convert causal to causal_bottom_right in inference when KV-caching is in use
                # so users can run with the same attn_mask_type for training and inference
                if attn_mask_type in ["causal", "padding_causal"]:
                    attn_mask_type = attn_mask_type + "_bottom_right"

5645
5646
5647
                if qkv_format == "bshd":
                    key_layer = key_layer.transpose(0, 1)
                    value_layer = value_layer.transpose(0, 1)
5648

5649
5650
5651
5652
                (
                    inference_key_memory,
                    inference_value_memory,
                ) = inference_params.key_value_memory_dict[self.layer_number]
5653

5654
5655
5656
                batch_start = inference_params.batch_size_offset
                batch_end = batch_start + key_layer.size(1)
                assert batch_end <= inference_key_memory.size(1)
5657

5658
5659
5660
                sequence_start = inference_params.sequence_len_offset
                sequence_end = sequence_start + key_layer.size(0)
                assert sequence_end <= inference_key_memory.size(0)
5661

5662
5663
5664
5665
5666
5667
5668
5669
5670
                # Copy keys and values into KV-cache
                inference_key_memory[sequence_start:sequence_end, batch_start:batch_end, ...] = (
                    key_layer
                )
                inference_value_memory[sequence_start:sequence_end, batch_start:batch_end, ...] = (
                    value_layer
                )
                key_layer = inference_key_memory[:sequence_end, batch_start:batch_end, ...]
                value_layer = inference_value_memory[:sequence_end, batch_start:batch_end, ...]
5671

5672
5673
5674
                if qkv_format == "bshd":
                    key_layer = key_layer.transpose(0, 1)
                    value_layer = value_layer.transpose(0, 1)
5675

5676
5677
                key_layer = key_layer.contiguous()
                value_layer = value_layer.contiguous()
5678
5679

            assert (
5680
5681
                key_layer.shape[-2] == self.num_gqa_groups_per_partition
                and value_layer.shape[-2] == self.num_gqa_groups_per_partition
5682
5683
5684
5685
            ), (
                "Keys and values must have num_gqa_group ="
                f" {self.num_gqa_groups_per_partition} heads!"
            )
5686
5687
5688
5689
5690
5691
5692
            assert qkv_format in [
                "sbhd",
                "bshd",
                "thd",
            ], "DotProductAttention only supports qkv_format = {'sbhd', 'bshd', 'thd'}!"

            if qkv_format == "thd":
5693
                assert all(
5694
5695
5696
5697
5698
5699
5700
5701
5702
5703
5704
5705
5706
                    len(x.shape) == 3 for x in (query_layer, key_layer, value_layer)
                ), "Queries, keys and values must be 3D tensors when qkv_format = thd!"
                assert (
                    cu_seqlens_q is not None and cu_seqlens_kv is not None
                ), "cu_seqlens_q and cu_seqlens_kv can not be None when qkv_format = thd!"
                assert (
                    cu_seqlens_q.shape == cu_seqlens_kv.shape
                    and len(cu_seqlens_q.shape) == 1
                    and len(cu_seqlens_kv.shape) == 1
                ), "cu_seqlens_q and cu_seqlens_q must both have shape [batch_size + 1]!"
                assert (
                    cu_seqlens_q.dtype == torch.int32 and cu_seqlens_kv.dtype == torch.int32
                ), "cu_seqlens_q and cu_seqlens_q must both be in dtype torch.int32!"
5707
                batch_size = len(cu_seqlens_q) - 1
5708
                if max_seqlen_q is None:
5709
5710
5711
5712
                    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]
5713
                    max_seqlen_q = int((seqlens_q.max().item() + 63) // 64 * 64)
5714
                if max_seqlen_kv is None:
5715
5716
5717
5718
                    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]
5719
                    max_seqlen_kv = int((seqlens_kv.max().item() + 63) // 64 * 64)
5720

5721
5722
5723
5724
5725
5726
            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)
5727
5728
            context_parallel = cp_size > 1

5729
            if qkv_format in ["sbhd", "bshd"]:
5730
                assert all(
5731
5732
5733
                    len(x.shape) == 4 for x in (query_layer, key_layer, value_layer)
                ), f"Queries, keys and values must be 4D tensors when qkv_format = {qkv_format}!"
                if qkv_format == "sbhd":
5734
5735
                    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
5736
                    batch_size = query_layer.shape[1]
5737
                else:
5738
5739
                    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
5740
                    batch_size = query_layer.shape[0]
5741
5742
                max_seqlen_q *= cp_size
                max_seqlen_kv *= cp_size
5743
5744
5745
5746
5747
                if cu_seqlens_q is not None:
                    seqlens_q = cu_seqlens_q[1:] - cu_seqlens_q[:-1]
                    assert all(
                        seqlens_q <= max_seqlen_q
                    ), """Sequence lengths indicated by cu_seqlens_q must be no greater than
5748
                        the sequence dimension in 'query_layer'!"""
5749
5750
5751
5752
5753
                if cu_seqlens_kv is not None:
                    seqlens_kv = cu_seqlens_kv[1:] - cu_seqlens_kv[:-1]
                    assert all(
                        seqlens_kv <= max_seqlen_kv
                    ), """Sequence lengths indicated by cu_seqlens_kv must be no greater than
5754
                        the sequence dimension in 'key_layer' and 'value_layer'!"""
5755
5756
5757
5758
5759
                if cu_seqlens_q is None or cu_seqlens_kv is None:
                    if "padding" in attn_mask_type:
                        assert (
                            attention_mask is not None
                        ), "Please provide attention_mask for padding!"
5760
                        if self.attention_type == "self":
5761
                            cu_seqlens_q = dpa_utils.get_cu_seqlens(attention_mask)
5762
5763
                            cu_seqlens_kv = cu_seqlens_q
                        else:
5764
5765
                            cu_seqlens_q = dpa_utils.get_cu_seqlens(attention_mask[0])
                            cu_seqlens_kv = dpa_utils.get_cu_seqlens(attention_mask[1])
5766
                    else:
5767
                        cu_seqlens_q = dpa_utils.get_full_cu_seqlens(
5768
5769
5770
5771
                            batch_size,
                            max_seqlen_q,
                            query_layer.device,
                        )
5772
                        cu_seqlens_kv = dpa_utils.get_full_cu_seqlens(
5773
5774
5775
5776
                            batch_size,
                            max_seqlen_kv,
                            key_layer.device,
                        )
5777

5778
5779
5780
5781
5782
            if (
                isinstance(query_layer, Float8Tensor)
                and isinstance(key_layer, Float8Tensor)
                and isinstance(value_layer, Float8Tensor)
            ):
5783
5784
5785
5786
                qkv_layout, query_layer._data, key_layer._data, value_layer._data = (
                    dpa_utils.get_qkv_layout(
                        query_layer._data, key_layer._data, value_layer._data, qkv_format=qkv_format
                    )
5787
5788
                )
            else:
5789
                qkv_layout, query_layer, key_layer, value_layer = dpa_utils.get_qkv_layout(
5790
5791
                    query_layer, key_layer, value_layer, qkv_format=qkv_format
                )
5792

5793
5794
5795
5796
5797
5798
5799
5800
            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
5801
            bottom_right_alignment = (attn_mask_type not in ["causal", "padding_causal"],)
5802
5803
5804
5805
5806
5807
5808
5809
            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
5810
                    or _alibi_cache["_bottom_right_alignment"] != bottom_right_alignment
5811
5812
5813
5814
5815
                    or _alibi_cache["_alibi_slopes"] is None
                ):
                    _alibi_cache["_alibi_slopes_require_update"] = True
                    _alibi_cache["_alibi_bias_require_update"] = True

5816
5817
            core_attention_bias_shape = None
            if core_attention_bias is not None:
5818
                if (
5819
5820
                    core_attention_bias.shape[0] == batch_size
                    and core_attention_bias.shape[1] == query_layer.shape[-2]
5821
                ):
5822
5823
5824
5825
5826
5827
5828
5829
5830
5831
5832
5833
5834
5835
5836
5837
5838
                    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"

5839
5840
5841
5842
5843
5844
5845
5846
5847
5848
5849
            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
5850

5851
            attention_params = dpa_utils.AttentionParams(
5852
5853
5854
5855
5856
5857
5858
5859
                qkv_type=type(query_layer),
                qkv_dtype=query_layer.dtype,
                qkv_layout=qkv_layout,
                batch_size=batch_size,
                num_heads=query_layer.shape[-2],
                num_gqa_groups=key_layer.shape[-2],
                max_seqlen_q=max_seqlen_q,
                max_seqlen_kv=max_seqlen_kv,
5860
5861
                head_dim_qk=query_layer.shape[-1],
                head_dim_v=value_layer.shape[-1],
5862
5863
5864
5865
5866
5867
5868
5869
5870
5871
5872
                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,
5873
5874
                deterministic=self.deterministic,
                is_training=self.training,
5875
5876
5877
                fp8=self.fp8,
                fp8_meta=self.fp8_meta,
            )
5878
            global _attention_backends
5879
5880
5881
5882
5883
5884
5885
            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"]:
5886
                fa_utils.use_v3 = fa_utils.v3_is_installed
5887
5888
5889
5890
5891
5892
                (
                    use_flash_attention,
                    use_fused_attention,
                    fused_attention_backend,
                    use_unfused_attention,
                    _,
5893
5894
5895
5896
5897
5898
5899
5900
                ) = 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
                _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
5901
                if use_flash_attention:
5902
5903
                    self.logger.info(
                        "Running with FlashAttention backend (version %s)",
5904
                        fa_utils.version if not fa_utils.use_v3 else fa_utils.fa3_version,
5905
                    )
5906
5907
5908
5909
                elif use_fused_attention:
                    self.logger.info(
                        "Running with FusedAttention backend (sub-backend %s)",
                        int(fused_attention_backend),
5910
                    )
5911
5912
5913
5914
5915
5916
5917
                elif use_unfused_attention:
                    self.logger.info("Running with UnfusedDotProductAttention backend")
            else:
                use_flash_attention = _attention_backends["use_flash_attention"]
                use_fused_attention = _attention_backends["use_fused_attention"]
                fused_attention_backend = _attention_backends["fused_attention_backend"]
                use_unfused_attention = _attention_backends["use_unfused_attention"]
5918

5919
5920
            if use_flash_attention:
                if core_attention_bias_type == "alibi":
5921
5922
                    alibi_slopes, _ = dpa_utils.get_alibi(
                        _alibi_cache,
5923
5924
5925
5926
5927
5928
5929
5930
5931
5932
5933
5934
5935
5936
5937
5938
5939
5940
5941
                        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,
5942
                    cp_comm_type=self.cp_comm_type,
5943
5944
                    max_seqlen_q=max_seqlen_q,
                    max_seqlen_kv=max_seqlen_kv,
5945
5946
                    fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                    fp8_meta=self.fp8_meta,
5947
                    quantizers=self.quantizers,
5948
                )
5949

5950
            if use_fused_attention:
5951
5952
                fu_core_attention_bias_type = core_attention_bias_type
                fu_core_attention_bias = core_attention_bias
5953
5954
5955
                if core_attention_bias_type == "alibi" and (
                    alibi_slopes is not None or max_seqlen_q != max_seqlen_kv
                ):
5956
                    fu_core_attention_bias_type = "post_scale_bias"
5957
5958
                    _, fu_core_attention_bias = dpa_utils.get_alibi(
                        _alibi_cache,
5959
5960
5961
5962
5963
                        query_layer.shape[-2],
                        max_seqlen_q,
                        max_seqlen_kv,
                        alibi_slopes=alibi_slopes,
                        bias_dtype=query_layer.dtype,
5964
                        bottom_right_alignment=attn_mask_type not in ["causal", "padding_causal"],
5965
                    )
5966
5967
5968
5969
5970
5971
5972
5973
5974
                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,
5975
5976
                        cu_seqlens_q_padded=cu_seqlens_q_padded,
                        cu_seqlens_kv_padded=cu_seqlens_kv_padded,
5977
5978
5979
5980
                        max_seqlen_q=max_seqlen_q,
                        max_seqlen_kv=max_seqlen_kv,
                        attn_mask_type=attn_mask_type,
                        attention_mask=attention_mask,
5981
                        window_size=window_size,
5982
5983
5984
5985
5986
5987
5988
                        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,
5989
                        cp_comm_type=self.cp_comm_type,
5990
5991
                        fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                        fp8_meta=self.fp8_meta,
5992
                        pad_between_seqs=pad_between_seqs,
5993
5994
                    )
                return self.fused_attention(
5995
5996
5997
5998
5999
6000
                    query_layer,
                    key_layer,
                    value_layer,
                    qkv_layout=qkv_layout,
                    cu_seqlens_q=cu_seqlens_q,
                    cu_seqlens_kv=cu_seqlens_kv,
6001
6002
                    cu_seqlens_q_padded=cu_seqlens_q_padded,
                    cu_seqlens_kv_padded=cu_seqlens_kv_padded,
6003
6004
                    max_seqlen_q=max_seqlen_q,
                    max_seqlen_kv=max_seqlen_kv,
6005
6006
                    attn_mask_type=attn_mask_type,
                    attention_mask=attention_mask,
6007
                    window_size=window_size,
6008
                    fused_attention_backend=fused_attention_backend,
6009
6010
                    core_attention_bias_type=fu_core_attention_bias_type,
                    core_attention_bias=fu_core_attention_bias,
6011
6012
6013
6014
                    fast_zero_fill=fast_zero_fill,
                    cp_group=self.cp_group,
                    cp_global_ranks=self.cp_global_ranks,
                    cp_stream=self.cp_stream,
6015
                    cp_comm_type=self.cp_comm_type,
6016
6017
                    fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                    fp8_meta=self.fp8_meta,
6018
                    quantizers=self.quantizers,
6019
                    pad_between_seqs=pad_between_seqs,
6020
                )
6021

6022
            from .cpu_offload import CPUOffloadEnabled
6023

6024
6025
6026
6027
6028
            if CPUOffloadEnabled:
                warnings.warn(
                    "Attention activation Offloading is only implemented"
                    "with Flash Attention and Fused Attention!"
                )
6029

6030
6031
6032
6033
6034
6035
6036
6037
6038
6039
6040
6041
            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,
6042
                        window_size=window_size,
6043
6044
6045
6046
6047
                        core_attention_bias_type=core_attention_bias_type,
                        core_attention_bias=core_attention_bias,
                        alibi_slopes=alibi_slopes,
                    )
                return self.unfused_attention(
6048
6049
6050
                    query_layer,
                    key_layer,
                    value_layer,
6051
6052
6053
6054
6055
                    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,
6056
                    window_size=window_size,
6057
6058
6059
6060
                    core_attention_bias_type=core_attention_bias_type,
                    core_attention_bias=core_attention_bias,
                    alibi_slopes=alibi_slopes,
                )
6061

6062
            raise ValueError("No dot product attention support for the provided inputs!")
6063
6064


6065
6066
6067
6068
6069
6070
6071
class MultiheadAttention(torch.nn.Module):
    r"""
    Multi-head Attention (MHA), including Query,
    Key, Value and Output projection.

    .. note::

6072
6073
        Argument :attr:`attention_mask` in the `forward` call is only used when
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
6074

6075
6076
6077
6078
6079
6080
6081
6082
6083
6084
6085
6086
6087
6088
6089
6090
6091
6092
6093
6094
6095
6096
6097
6098
6099
    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.
6100
6101
    attn_mask_type: {'no_mask', 'padding', 'causal', 'padding_causal', 'causal_bottom_right',
                   'padding_causal_bottom_right','arbitrary'},
6102
                   default = `causal`
6103
6104
6105
6106
6107
                   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.
6108
6109
6110
6111
    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
6112
6113
6114
                window and causal mask specifically. Both `causal` and `causal_bottom_right` masks
                map to `window_size = (-1, 0)` and Transformer Engine distinguishes them based on
                `attn_mask_type`. Similar to :attr:`attn_mask_type`, `window_size` can
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                be overridden by :attr:`window_size` in `forward` as well.
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    num_gqa_groups : int, default = `None`
                         number of GQA groups in the transformer layer.
                         Grouped Query Attention is described in
                         `this paper <https://arxiv.org/pdf/2305.13245.pdf>`_.
                         This only affects the keys and values, not the querys.
                         GQA-1 is equivalent to Multi-Query Attention
                         (`MQA <https://arxiv.org/pdf/1911.02150.pdf>`_), while GQA-H
                         is equivalent to MHA, i.e. `num_gqa_groups = num_attention_heads`.
    return_layernorm_output : bool, default = `False`
                             if set to `True`, output of layernorm is returned from the forward
                             together with the output of the linear transformation.
                             Example use case: residual connection for transformer module is
                             taken post layernorm.
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    input_layernorm: bool, default = `False`
                     if set to `True`, layer normalization to the input is applied.
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    attention_type: { 'self', 'cross' }, default = 'self'
                   type of attention applied.
    zero_centered_gamma : bool, default = 'False'
                         if set to 'True', gamma parameter in LayerNorm is initialized to 0 and
                         the LayerNorm formula changes to

                         .. math::
                            y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \varepsilon}} *
                            (1 + \gamma) + \beta
    normalization : { 'LayerNorm', 'RMSNorm' }, default = 'LayerNorm'
                   type of normalization applied.
    qkv_weight_interleaved : bool, default = `True`
                            if set to `False`, the QKV weight is interpreted as a concatenation of
                            query, key, and value weights along the `0th` dimension. The default
                            interpretation is that the individual `q`, `k`, and `v` weights for each
                            attention head are interleaved. This parameter is set to `False` when
                            using :attr:`fuse_qkv_params=False`.
    bias : bool, default = `True`
          if set to `False`, the transformer layer will not learn any additive biases.
    device : Union[torch.device, str], default = "cuda"
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          The device on which the parameters of the model will be allocated. It is the user's
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          responsibility to ensure all parameters are moved to the GPU before running the
          forward pass.
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    qkv_format: str, default = `sbhd`
            dimension format for `query_layer`, `key_layer` and `value_layer`,
            {`sbhd`, `bshd`}. `s` stands for the sequence length, `b` batch size,
            `h` the number of heads and `d` head size. `sbhd` and `bshd` formats
            are used for when sequences in a batch are of equal length or padded to
            equal length. Please note that these formats do not reflect how
            tensors `query_layer`, `key_layer`, `value_layer` are laid out in memory.
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            For that, please use `get_qkv_layout` to gain the layout information.
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    Parallelism parameters
    ----------------------
    set_parallel_mode : bool, default = `False`
                      if set to `True`, QKV and FC1 layers are used as Column Parallel
                      whereas PROJ and FC2 is used as Row Parallel as described
                      `here <https://arxiv.org/pdf/1909.08053.pdf>`_.
    sequence_parallel : bool, default = `False`
                       if set to `True`, uses sequence parallelism.
    tp_group : ProcessGroup, default = `None`
              tensor parallel process group.
    tp_size : int, default = 1
             used as TP (tensor parallel) world size when TP groups are not formed during
             initialization. In this case, users must call the
             `set_tensor_parallel_group(tp_group)` method on the initialized module before the
             forward pass to supply the tensor parallel group needed for tensor and sequence
             parallel collectives.

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

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

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

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

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

        qkv_parallel_mode = "column" if set_parallel_mode else None

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

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

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

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

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

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

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

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

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        # Deep iterate but skip self to avoid infinite recursion.
        for index, child in enumerate(self.modules()):
            if index == 0:
                continue
            if hasattr(child, "set_context_parallel_group"):
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                child.set_context_parallel_group(cp_group, cp_global_ranks, cp_stream, cp_comm_type)
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    def forward(
        self,
        hidden_states: torch.Tensor,
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        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
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        encoder_output: Optional[torch.Tensor] = None,
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        attn_mask_type: Optional[str] = None,
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        window_size: Optional[Tuple[int, int]] = None,
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        is_first_microbatch: Optional[bool] = None,
        checkpoint_core_attention: bool = False,
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        inference_params: Optional[InferenceParams] = None,
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        rotary_pos_emb: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
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        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
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        alibi_slopes: Optional[torch.Tensor] = None,
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        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
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        fast_zero_fill: bool = True,
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        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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        core_attention_bias: Optional[torch.Tensor], default = `None`
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                    Bias tensor for Q * K.T, shape [1, num_head, max_seqlen_q, max_seqlen_kv].
                    It should be 'None' for 'no_bias' and 'alibi' bias types.
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        alibi_slopes: Optional[torch.Tensor], default = `None`
                     ALiBi slopes in FP32 and shape [nheads] or [batch_size, nheads].
                     It adds a bias of (-alibi_slope * (i + seqlen_k - seqlen_q - j))
                     to the attention score of query i and key j.
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        cu_seqlens_q: Optional[torch.Tensor], default = `None`
                   Cumulative sum of sequence lengths (without offset) in a batch for `query_layer`,
                   with shape [batch_size + 1] and dtype torch.int32.
        cu_seqlens_kv: Optional[torch.Tensor], default = `None`
                   Cumulative sum of sequence lengths (without offset) in a batch for `key_layer`
                   and `value_layer`, with shape [batch_size + 1] and dtype torch.int32.
        max_seqlen_q: Optional[int], default = `None`
                      Maximum sequence length in `query_layer`.
                      Calculated from `cu_seqlens_q` if not provided.
        max_seqlen_kv: Optional[int], default = `None`
                       Maximum sequence length in `key_layer` and `value_layer`.
                       Calculated from `cu_seqlens_kv` if not provided.
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        fast_zero_fill: bool, default = `True`
                    Whether to set output tensors to 0 or not before use.
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        pad_between_seqs: Optional[bool], default = `None`
            If None, inferred from qkv_format, cu_seqlens and cu_seqlens_padded.
            If true, there are padding tokens between individual sequences in a packed batch.
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        """
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        # hidden_states: [sq, b, h]

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

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

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

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

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

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

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

            mixed_kv_layer = mixed_kv_layer.view(*new_tensor_shape)

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

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

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

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

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

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